Marine Vehicle Sensor Network Architecture and Protocol Designs for Ocean Observation

PubMed Central

Zhang, Shaowei; Yu, Jiancheng; Zhang, Aiqun; Yang, Lei; Shu, Yeqiang

2012-01-01

The micro-scale and meso-scale ocean dynamic processes which are nonlinear and have large variability, have a significant impact on the fisheries, natural resources, and marine climatology. A rapid, refined and sophisticated observation system is therefore needed in marine scientific research. The maneuverability and controllability of mobile sensor platforms make them a preferred choice to establish ocean observing networks, compared to the static sensor observing platform. In this study, marine vehicles are utilized as the nodes of mobile sensor networks for coverage sampling of a regional ocean area and ocean feature tracking. A synoptic analysis about marine vehicle dynamic control, multi vehicles mission assignment and path planning methods, and ocean feature tracking and observing techniques is given. Combined with the observation plan in the South China Sea, we provide an overview of the mobile sensor networks established with marine vehicles, and the corresponding simulation results. PMID:22368475

Nonlinear Dynamic Model-Based Multiobjective Sensor Network Design Algorithm for a Plant with an Estimator-Based Control System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Paul, Prokash; Bhattacharyya, Debangsu; Turton, Richard

Here, a novel sensor network design (SND) algorithm is developed for maximizing process efficiency while minimizing sensor network cost for a nonlinear dynamic process with an estimator-based control system. The multiobjective optimization problem is solved following a lexicographic approach where the process efficiency is maximized first followed by minimization of the sensor network cost. The partial net present value, which combines the capital cost due to the sensor network and the operating cost due to deviation from the optimal efficiency, is proposed as an alternative objective. The unscented Kalman filter is considered as the nonlinear estimator. The large-scale combinatorial optimizationmore » problem is solved using a genetic algorithm. The developed SND algorithm is applied to an acid gas removal (AGR) unit as part of an integrated gasification combined cycle (IGCC) power plant with CO 2 capture. Due to the computational expense, a reduced order nonlinear model of the AGR process is identified and parallel computation is performed during implementation.« less

Nonlinear Dynamic Model-Based Multiobjective Sensor Network Design Algorithm for a Plant with an Estimator-Based Control System

DOE PAGES

Paul, Prokash; Bhattacharyya, Debangsu; Turton, Richard; ...

2017-06-06

Here, a novel sensor network design (SND) algorithm is developed for maximizing process efficiency while minimizing sensor network cost for a nonlinear dynamic process with an estimator-based control system. The multiobjective optimization problem is solved following a lexicographic approach where the process efficiency is maximized first followed by minimization of the sensor network cost. The partial net present value, which combines the capital cost due to the sensor network and the operating cost due to deviation from the optimal efficiency, is proposed as an alternative objective. The unscented Kalman filter is considered as the nonlinear estimator. The large-scale combinatorial optimizationmore » problem is solved using a genetic algorithm. The developed SND algorithm is applied to an acid gas removal (AGR) unit as part of an integrated gasification combined cycle (IGCC) power plant with CO 2 capture. Due to the computational expense, a reduced order nonlinear model of the AGR process is identified and parallel computation is performed during implementation.« less

Simple Random Sampling-Based Probe Station Selection for Fault Detection in Wireless Sensor Networks

PubMed Central

Huang, Rimao; Qiu, Xuesong; Rui, Lanlan

2011-01-01

Fault detection for wireless sensor networks (WSNs) has been studied intensively in recent years. Most existing works statically choose the manager nodes as probe stations and probe the network at a fixed frequency. This straightforward solution leads however to several deficiencies. Firstly, by only assigning the fault detection task to the manager node the whole network is out of balance, and this quickly overloads the already heavily burdened manager node, which in turn ultimately shortens the lifetime of the whole network. Secondly, probing with a fixed frequency often generates too much useless network traffic, which results in a waste of the limited network energy. Thirdly, the traditional algorithm for choosing a probing node is too complicated to be used in energy-critical wireless sensor networks. In this paper, we study the distribution characters of the fault nodes in wireless sensor networks, validate the Pareto principle that a small number of clusters contain most of the faults. We then present a Simple Random Sampling-based algorithm to dynamic choose sensor nodes as probe stations. A dynamic adjusting rule for probing frequency is also proposed to reduce the number of useless probing packets. The simulation experiments demonstrate that the algorithm and adjusting rule we present can effectively prolong the lifetime of a wireless sensor network without decreasing the fault detected rate. PMID:22163789

Simple random sampling-based probe station selection for fault detection in wireless sensor networks.

PubMed

Huang, Rimao; Qiu, Xuesong; Rui, Lanlan

2011-01-01

Fault detection for wireless sensor networks (WSNs) has been studied intensively in recent years. Most existing works statically choose the manager nodes as probe stations and probe the network at a fixed frequency. This straightforward solution leads however to several deficiencies. Firstly, by only assigning the fault detection task to the manager node the whole network is out of balance, and this quickly overloads the already heavily burdened manager node, which in turn ultimately shortens the lifetime of the whole network. Secondly, probing with a fixed frequency often generates too much useless network traffic, which results in a waste of the limited network energy. Thirdly, the traditional algorithm for choosing a probing node is too complicated to be used in energy-critical wireless sensor networks. In this paper, we study the distribution characters of the fault nodes in wireless sensor networks, validate the Pareto principle that a small number of clusters contain most of the faults. We then present a Simple Random Sampling-based algorithm to dynamic choose sensor nodes as probe stations. A dynamic adjusting rule for probing frequency is also proposed to reduce the number of useless probing packets. The simulation experiments demonstrate that the algorithm and adjusting rule we present can effectively prolong the lifetime of a wireless sensor network without decreasing the fault detected rate.

Dynamic Hierarchical Energy-Efficient Method Based on Combinatorial Optimization for Wireless Sensor Networks.

PubMed

Chang, Yuchao; Tang, Hongying; Cheng, Yongbo; Zhao, Qin; Yuan, Baoqing Li andXiaobing

2017-07-19

Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs). Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO) to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum-minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), genetic protocol-based self-organizing network clustering (GASONeC), and double cost function-based routing (DCFR) algorithms.

A low cost strategy to monitor the expansion and contraction of the flowing stream network in mountainous headwater catchments

NASA Astrophysics Data System (ADS)

Assendelft, Rick; van Meerveld, Ilja; Seibert, Jan

2017-04-01

Streams are dynamic features in the landscape. The flowing stream network expands and contracts, connects and disconnects in response to rainfall events and seasonal changes in catchment wetness. Sections of the river system that experience these wet and dry cycles are often referred to as temporary streams. Temporary streams are abundant and widely distributed freshwater ecosystems. They account for more than half of the total length of the global stream network, are unique habitats and form important hydrological and ecological links between the uplands and perennial streams. However, temporary streams have been largely unstudied, especially in mountainous headwater catchments. The dynamic character of these systems makes it difficult to monitor them. We describe a low-cost, do-it-yourself strategy to monitor the occurrence of water and flow in temporary streams. We evaluate this strategy in two headwater catchments in Switzerland. The low cost sensor network consists of electrical resistivity sensors, water level switches, temperature sensors and flow sensors. These sensors are connected to Arduino microcontrollers and data loggers, which log the data every 5 minutes. The data from the measurement network are compared with observations (mapping of the temporary stream network) as well as time lapse camera data to evaluate the performance of the sensors. We look at how frequently the output of the sensors (presence and absence of water from the ER and water level data, and flow or no-flow from the flow sensors) corresponds to the observed channel state. This is done for each sensor, per sub-catchment, per precipitation event and per sensor location to determine the best sensor combination to monitor temporary streams in mountainous catchments and in which situation which sensor combination works best. The preliminary results show that the sensors and monitoring network work well. The data from the sensors corresponds with the observations and provides information on the expansion of the stream network pattern.

An emergency-adaptive routing scheme for wireless sensor networks for building fire hazard monitoring.

PubMed

Zeng, Yuanyuan; Sreenan, Cormac J; Sitanayah, Lanny; Xiong, Naixue; Park, Jong Hyuk; Zheng, Guilin

2011-01-01

Fire hazard monitoring and evacuation for building environments is a novel application area for the deployment of wireless sensor networks. In this context, adaptive routing is essential in order to ensure safe and timely data delivery in building evacuation and fire fighting resource applications. Existing routing mechanisms for wireless sensor networks are not well suited for building fires, especially as they do not consider critical and dynamic network scenarios. In this paper, an emergency-adaptive, real-time and robust routing protocol is presented for emergency situations such as building fire hazard applications. The protocol adapts to handle dynamic emergency scenarios and works well with the routing hole problem. Theoretical analysis and simulation results indicate that our protocol provides a real-time routing mechanism that is well suited for dynamic emergency scenarios in building fires when compared with other related work.

An Emergency-Adaptive Routing Scheme for Wireless Sensor Networks for Building Fire Hazard Monitoring

PubMed Central

Zeng, Yuanyuan; Sreenan, Cormac J.; Sitanayah, Lanny; Xiong, Naixue; Park, Jong Hyuk; Zheng, Guilin

2011-01-01

Fire hazard monitoring and evacuation for building environments is a novel application area for the deployment of wireless sensor networks. In this context, adaptive routing is essential in order to ensure safe and timely data delivery in building evacuation and fire fighting resource applications. Existing routing mechanisms for wireless sensor networks are not well suited for building fires, especially as they do not consider critical and dynamic network scenarios. In this paper, an emergency-adaptive, real-time and robust routing protocol is presented for emergency situations such as building fire hazard applications. The protocol adapts to handle dynamic emergency scenarios and works well with the routing hole problem. Theoretical analysis and simulation results indicate that our protocol provides a real-time routing mechanism that is well suited for dynamic emergency scenarios in building fires when compared with other related work. PMID:22163774

Data aggregation in wireless sensor networks using the SOAP protocol

NASA Astrophysics Data System (ADS)

Al-Yasiri, A.; Sunley, A.

2007-07-01

Wireless sensor networks (WSN) offer an increasingly attractive method of data gathering in distributed system architectures and dynamic access via wireless connectivity. Wireless sensor networks have physical and resource limitations, this leads to increased complexity for application developers and often results in applications that are closely coupled with network protocols. In this paper, a data aggregation framework using SOAP (Simple Object Access Protocol) on wireless sensor networks is presented. The framework works as a middleware for aggregating data measured by a number of nodes within a network. The aim of the study is to assess the suitability of the protocol in such environments where resources are limited compared to traditional networks.

Supervisory control of mobile sensor networks: math formulation, simulation, and implementation.

PubMed

Giordano, Vincenzo; Ballal, Prasanna; Lewis, Frank; Turchiano, Biagio; Zhang, Jing Bing

2006-08-01

This paper uses a novel discrete-event controller (DEC) for the coordination of cooperating heterogeneous wireless sensor networks (WSNs) containing both unattended ground sensors (UGSs) and mobile sensor robots. The DEC sequences the most suitable tasks for each agent and assigns sensor resources according to the current perception of the environment. A matrix formulation makes this DEC particularly useful for WSN, where missions change and sensor agents may be added or may fail. WSN have peculiarities that complicate their supervisory control. Therefore, this paper introduces several new tools for DEC design and operation, including methods for generating the required supervisory matrices based on mission planning, methods for modifying the matrices in the event of failed nodes, or nodes entering the network, and a novel dynamic priority assignment weighting approach for selecting the most appropriate and useful sensors for a given mission task. The resulting DEC represents a complete dynamical description of the WSN system, which allows a fast programming of deployable WSN, a computer simulation analysis, and an efficient implementation. The DEC is actually implemented on an experimental wireless-sensor-network prototyping system. Both simulation and experimental results are presented to show the effectiveness and versatility of the developed control architecture.

An Improved Co-evolutionary Particle Swarm Optimization for Wireless Sensor Networks with Dynamic Deployment

PubMed Central

Wang, Xue; Wang, Sheng; Ma, Jun-Jie

2007-01-01

The effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment, which is usually supported by the virtual force (VF) algorithm. However, in the VF algorithm, the virtual force exerted by stationary sensor nodes will hinder the movement of mobile sensor nodes. Particle swarm optimization (PSO) is introduced as another dynamic deployment algorithm, but in this case the computation time required is the big bottleneck. This paper proposes a dynamic deployment algorithm which is named “virtual force directed co-evolutionary particle swarm optimization” (VFCPSO), since this algorithm combines the co-evolutionary particle swarm optimization (CPSO) with the VF algorithm, whereby the CPSO uses multiple swarms to optimize different components of the solution vectors for dynamic deployment cooperatively and the velocity of each particle is updated according to not only the historical local and global optimal solutions, but also the virtual forces of sensor nodes. Simulation results demonstrate that the proposed VFCPSO is competent for dynamic deployment in WSNs and has better performance with respect to computation time and effectiveness than the VF, PSO and VFPSO algorithms.

Gossip and Distributed Kalman Filtering: Weak Consensus Under Weak Detectability

NASA Astrophysics Data System (ADS)

Kar, Soummya; Moura, José M. F.

2011-04-01

The paper presents the gossip interactive Kalman filter (GIKF) for distributed Kalman filtering for networked systems and sensor networks, where inter-sensor communication and observations occur at the same time-scale. The communication among sensors is random; each sensor occasionally exchanges its filtering state information with a neighbor depending on the availability of the appropriate network link. We show that under a weak distributed detectability condition: 1. the GIKF error process remains stochastically bounded, irrespective of the instability properties of the random process dynamics; and 2. the network achieves \\emph{weak consensus}, i.e., the conditional estimation error covariance at a (uniformly) randomly selected sensor converges in distribution to a unique invariant measure on the space of positive semi-definite matrices (independent of the initial state.) To prove these results, we interpret the filtered states (estimates and error covariances) at each node in the GIKF as stochastic particles with local interactions. We analyze the asymptotic properties of the error process by studying as a random dynamical system the associated switched (random) Riccati equation, the switching being dictated by a non-stationary Markov chain on the network graph.

Spatiotemporal models for data-anomaly detection in dynamic environmental monitoring campaigns

Treesearch

E.W. Dereszynski; T.G. Dietterich

2011-01-01

The ecological sciences have benefited greatly from recent advances in wireless sensor technologies. These technologies allow researchers to deploy networks of automated sensors, which can monitor a landscape at very fine temporal and spatial scales. However, these networks are subject to harsh conditions, which lead to malfunctions in individual sensors and failures...

TinyONet: A Cache-Based Sensor Network Bridge Enabling Sensing Data Reusability and Customized Wireless Sensor Network Services

PubMed Central

Jung, Eui-Hyun; Park, Yong-Jin

2008-01-01

In recent years, a few protocol bridge research projects have been announced to enable a seamless integration of Wireless Sensor Networks (WSNs) with the TCP/IP network. These studies have ensured the transparent end-to-end communication between two network sides in the node-centric manner. Researchers expect this integration will trigger the development of various application domains. However, prior research projects have not fully explored some essential features for WSNs, especially the reusability of sensing data and the data-centric communication. To resolve these issues, we suggested a new protocol bridge system named TinyONet. In TinyONet, virtual sensors play roles as virtual counterparts of physical sensors and they dynamically group to make a functional entity, Slice. Instead of direct interaction with individual physical sensors, each sensor application uses its own WSN service provided by Slices. If a new kind of service is required in TinyONet, the corresponding function can be dynamically added at runtime. Beside the data-centric communication, it also supports the node-centric communication and the synchronous access. In order to show the effectiveness of the system, we implemented TinyONet on an embedded Linux machine and evaluated it with several experimental scenarios. PMID:27873968

Dynamic Tasking of Networked Sensors Using Covariance Information

DTIC Science & Technology

2010-09-01

has been created under an effort called TASMAN (Tasking Autonomous Sensors in a Multiple Application Network). One of the first studies utilizing this...environment was focused on a novel resource management approach, namely covariance-based tasking. Under this scheme, the state error covariance of...resident space objects (RSO), sensor characteristics, and sensor- target geometry were used to determine the effectiveness of future observations in

Dynamic Hierarchical Energy-Efficient Method Based on Combinatorial Optimization for Wireless Sensor Networks

PubMed Central

Tang, Hongying; Cheng, Yongbo; Zhao, Qin; Li, Baoqing; Yuan, Xiaobing

2017-01-01

Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs). Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO) to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum–minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), genetic protocol-based self-organizing network clustering (GASONeC), and double cost function-based routing (DCFR) algorithms. PMID:28753962

A Data-Driven Response Virtual Sensor Technique with Partial Vibration Measurements Using Convolutional Neural Network.

PubMed

Sun, Shan-Bin; He, Yuan-Yuan; Zhou, Si-Da; Yue, Zhen-Jiang

2017-12-12

Measurement of dynamic responses plays an important role in structural health monitoring, damage detection and other fields of research. However, in aerospace engineering, the physical sensors are limited in the operational conditions of spacecraft, due to the severe environment in outer space. This paper proposes a virtual sensor model with partial vibration measurements using a convolutional neural network. The transmissibility function is employed as prior knowledge. A four-layer neural network with two convolutional layers, one fully connected layer, and an output layer is proposed as the predicting model. Numerical examples of two different structural dynamic systems demonstrate the performance of the proposed approach. The excellence of the novel technique is further indicated using a simply supported beam experiment comparing to a modal-model-based virtual sensor, which uses modal parameters, such as mode shapes, for estimating the responses of the faulty sensors. The results show that the presented data-driven response virtual sensor technique can predict structural response with high accuracy.

A Data-Driven Response Virtual Sensor Technique with Partial Vibration Measurements Using Convolutional Neural Network

PubMed Central

Sun, Shan-Bin; He, Yuan-Yuan; Zhou, Si-Da; Yue, Zhen-Jiang

2017-01-01

Measurement of dynamic responses plays an important role in structural health monitoring, damage detection and other fields of research. However, in aerospace engineering, the physical sensors are limited in the operational conditions of spacecraft, due to the severe environment in outer space. This paper proposes a virtual sensor model with partial vibration measurements using a convolutional neural network. The transmissibility function is employed as prior knowledge. A four-layer neural network with two convolutional layers, one fully connected layer, and an output layer is proposed as the predicting model. Numerical examples of two different structural dynamic systems demonstrate the performance of the proposed approach. The excellence of the novel technique is further indicated using a simply supported beam experiment comparing to a modal-model-based virtual sensor, which uses modal parameters, such as mode shapes, for estimating the responses of the faulty sensors. The results show that the presented data-driven response virtual sensor technique can predict structural response with high accuracy. PMID:29231868

A General theory of Signal Integration for Fault-Tolerant Dynamic Distributed Sensor Networks

DTIC Science & Technology

1993-10-01

related to a) the architecture and fault- tolerance of the distributed sensor network, b) the proper synchronisation of sensor signals, c) the...Computational complexities of the problem of distributed detection. 5) Issues related to recording of events and synchronization in distributed sensor...Intervals for Synchronization in Real Time Distributed Systems", Submitted to Electronic Encyclopedia. 3. V. G. Hegde and S. S. Iyengar "Efficient

Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Sensors: An Example of Space Weather Forecasting by Multiple Satellites

PubMed Central

Tokumitsu, Masahiro; Hasegawa, Keisuke; Ishida, Yoshiteru

2016-01-01

This paper attempts to construct a resilient sensor network model with an example of space weather forecasting. The proposed model is based on a dynamic relational network. Space weather forecasting is vital for a satellite operation because an operational team needs to make a decision for providing its satellite service. The proposed model is resilient to failures of sensors or missing data due to the satellite operation. In the proposed model, the missing data of a sensor is interpolated by other sensors associated. This paper demonstrates two examples of space weather forecasting that involves the missing observations in some test cases. In these examples, the sensor network for space weather forecasting continues a diagnosis by replacing faulted sensors with virtual ones. The demonstrations showed that the proposed model is resilient against sensor failures due to suspension of hardware failures or technical reasons. PMID:27092508

Resilient Sensor Networks with Spatiotemporal Interpolation of Missing Sensors: An Example of Space Weather Forecasting by Multiple Satellites.

PubMed

Tokumitsu, Masahiro; Hasegawa, Keisuke; Ishida, Yoshiteru

2016-04-15

This paper attempts to construct a resilient sensor network model with an example of space weather forecasting. The proposed model is based on a dynamic relational network. Space weather forecasting is vital for a satellite operation because an operational team needs to make a decision for providing its satellite service. The proposed model is resilient to failures of sensors or missing data due to the satellite operation. In the proposed model, the missing data of a sensor is interpolated by other sensors associated. This paper demonstrates two examples of space weather forecasting that involves the missing observations in some test cases. In these examples, the sensor network for space weather forecasting continues a diagnosis by replacing faulted sensors with virtual ones. The demonstrations showed that the proposed model is resilient against sensor failures due to suspension of hardware failures or technical reasons.

Model and Dynamic Behavior of Malware Propagation over Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Song, Yurong; Jiang, Guo-Ping

Based on the inherent characteristics of wireless sensor networks (WSN), the dynamic behavior of malware propagation in flat WSN is analyzed and investigated. A new model is proposed using 2-D cellular automata (CA), which extends the traditional definition of CA and establishes whole transition rules for malware propagation in WSN. Meanwhile, the validations of the model are proved through theoretical analysis and simulations. The theoretical analysis yields closed-form expressions which show good agreement with the simulation results of the proposed model. It is shown that the malware propaga-tion in WSN unfolds neighborhood saturation, which dominates the effects of increasing infectivity and limits the spread of the malware. MAC mechanism of wireless sensor networks greatly slows down the speed of malware propagation and reduces the risk of large-scale malware prevalence in these networks. The proposed model can describe accurately the dynamic behavior of malware propagation over WSN, which can be applied in developing robust and efficient defense system on WSN.

Efficient, Decentralized Detection of Qualitative Spatial Events in a Dynamic Scalar Field

PubMed Central

Jeong, Myeong-Hun; Duckham, Matt

2015-01-01

This paper describes an efficient, decentralized algorithm to monitor qualitative spatial events in a dynamic scalar field. The events of interest involve changes to the critical points (i.e., peak, pits and passes) and edges of the surface network derived from the field. Four fundamental types of event (appearance, disappearance, movement and switch) are defined. Our algorithm is designed to rely purely on qualitative information about the neighborhoods of nodes in the sensor network and does not require information about nodes’ coordinate positions. Experimental investigations confirm that our algorithm is efficient, with O(n) overall communication complexity (where n is the number of nodes in the sensor network), an even load balance and low operational latency. The accuracy of event detection is comparable to established centralized algorithms for the identification of critical points of a surface network. Our algorithm is relevant to a broad range of environmental monitoring applications of sensor networks. PMID:26343672

Efficient, Decentralized Detection of Qualitative Spatial Events in a Dynamic Scalar Field.

PubMed

Jeong, Myeong-Hun; Duckham, Matt

2015-08-28

This paper describes an efficient, decentralized algorithm to monitor qualitative spatial events in a dynamic scalar field. The events of interest involve changes to the critical points (i.e., peak, pits and passes) and edges of the surface network derived from the field. Four fundamental types of event (appearance, disappearance, movement and switch) are defined. Our algorithm is designed to rely purely on qualitative information about the neighborhoods of nodes in the sensor network and does not require information about nodes' coordinate positions. Experimental investigations confirm that our algorithm is efficient, with O(n) overall communication complexity (where n is the number of nodes in the sensor network), an even load balance and low operational latency. The accuracy of event detection is comparable to established centralized algorithms for the identification of critical points of a surface network. Our algorithm is relevant to a broad range of environmental monitoring applications of sensor networks.

Increasing the Lifetime of Mobile WSNs via Dynamic Optimization of Sensor Node Communication Activity.

PubMed

Guimarães, Dayan Adionel; Sakai, Lucas Jun; Alberti, Antonio Marcos; de Souza, Rausley Adriano Amaral

2016-09-20

In this paper, a simple and flexible method for increasing the lifetime of fixed or mobile wireless sensor networks is proposed. Based on past residual energy information reported by the sensor nodes, the sink node or another central node dynamically optimizes the communication activity levels of the sensor nodes to save energy without sacrificing the data throughput. The activity levels are defined to represent portions of time or time-frequency slots in a frame, during which the sensor nodes are scheduled to communicate with the sink node to report sensory measurements. Besides node mobility, it is considered that sensors' batteries may be recharged via a wireless power transmission or equivalent energy harvesting scheme, bringing to the optimization problem an even more dynamic character. We report large increased lifetimes over the non-optimized network and comparable or even larger lifetime improvements with respect to an idealized greedy algorithm that uses both the real-time channel state and the residual energy information.

System approach to distributed sensor management

NASA Astrophysics Data System (ADS)

Mayott, Gregory; Miller, Gordon; Harrell, John; Hepp, Jared; Self, Mid

2010-04-01

Since 2003, the US Army's RDECOM CERDEC Night Vision Electronic Sensor Directorate (NVESD) has been developing a distributed Sensor Management System (SMS) that utilizes a framework which demonstrates application layer, net-centric sensor management. The core principles of the design support distributed and dynamic discovery of sensing devices and processes through a multi-layered implementation. This results in a sensor management layer that acts as a System with defined interfaces for which the characteristics, parameters, and behaviors can be described. Within the framework, the definition of a protocol is required to establish the rules for how distributed sensors should operate. The protocol defines the behaviors, capabilities, and message structures needed to operate within the functional design boundaries. The protocol definition addresses the requirements for a device (sensors or processes) to dynamically join or leave a sensor network, dynamically describe device control and data capabilities, and allow dynamic addressing of publish and subscribe functionality. The message structure is a multi-tiered definition that identifies standard, extended, and payload representations that are specifically designed to accommodate the need for standard representations of common functions, while supporting the need for feature-based functions that are typically vendor specific. The dynamic qualities of the protocol enable a User GUI application the flexibility of mapping widget-level controls to each device based on reported capabilities in real-time. The SMS approach is designed to accommodate scalability and flexibility within a defined architecture. The distributed sensor management framework and its application to a tactical sensor network will be described in this paper.

Pollution source localization in an urban water supply network based on dynamic water demand.

PubMed

Yan, Xuesong; Zhu, Zhixin; Li, Tian

2017-10-27

Urban water supply networks are susceptible to intentional, accidental chemical, and biological pollution, which pose a threat to the health of consumers. In recent years, drinking-water pollution incidents have occurred frequently, seriously endangering social stability and security. The real-time monitoring for water quality can be effectively implemented by placing sensors in the water supply network. However, locating the source of pollution through the data detection obtained by water quality sensors is a challenging problem. The difficulty lies in the limited number of sensors, large number of water supply network nodes, and dynamic user demand for water, which leads the pollution source localization problem to an uncertainty, large-scale, and dynamic optimization problem. In this paper, we mainly study the dynamics of the pollution source localization problem. Previous studies of pollution source localization assume that hydraulic inputs (e.g., water demand of consumers) are known. However, because of the inherent variability of urban water demand, the problem is essentially a fluctuating dynamic problem of consumer's water demand. In this paper, the water demand is considered to be stochastic in nature and can be described using Gaussian model or autoregressive model. On this basis, an optimization algorithm is proposed based on these two dynamic water demand change models to locate the pollution source. The objective of the proposed algorithm is to find the locations and concentrations of pollution sources that meet the minimum between the analogue and detection values of the sensor. Simulation experiments were conducted using two different sizes of urban water supply network data, and the experimental results were compared with those of the standard genetic algorithm.

Optimal Power Control in Wireless Powered Sensor Networks: A Dynamic Game-Based Approach

PubMed Central

Xu, Haitao; Guo, Chao; Zhang, Long

2017-01-01

In wireless powered sensor networks (WPSN), it is essential to research uplink transmit power control in order to achieve throughput performance balancing and energy scheduling. Each sensor should have an optimal transmit power level for revenue maximization. In this paper, we discuss a dynamic game-based algorithm for optimal power control in WPSN. The main idea is to use the non-cooperative differential game to control the uplink transmit power of wireless sensors in WPSN, to extend their working hours and to meet QoS (Quality of Services) requirements. Subsequently, the Nash equilibrium solutions are obtained through Bellman dynamic programming. At the same time, an uplink power control algorithm is proposed in a distributed manner. Through numerical simulations, we demonstrate that our algorithm can obtain optimal power control and reach convergence for an infinite horizon. PMID:28282945

A Novel Distributed Privacy Paradigm for Visual Sensor Networks Based on Sharing Dynamical Systems

NASA Astrophysics Data System (ADS)

Luh, William; Kundur, Deepa; Zourntos, Takis

2006-12-01

Visual sensor networks (VSNs) provide surveillance images/video which must be protected from eavesdropping and tampering en route to the base station. In the spirit of sensor networks, we propose a novel paradigm for securing privacy and confidentiality in a distributed manner. Our paradigm is based on the control of dynamical systems, which we show is well suited for VSNs due to its low complexity in terms of processing and communication, while achieving robustness to both unintentional noise and intentional attacks as long as a small subset of nodes are affected. We also present a low complexity algorithm called TANGRAM to demonstrate the feasibility of applying our novel paradigm to VSNs. We present and discuss simulation results of TANGRAM.

A market-based optimization approach to sensor and resource management

NASA Astrophysics Data System (ADS)

Schrage, Dan; Farnham, Christopher; Gonsalves, Paul G.

2006-05-01

Dynamic resource allocation for sensor management is a problem that demands solutions beyond traditional approaches to optimization. Market-based optimization applies solutions from economic theory, particularly game theory, to the resource allocation problem by creating an artificial market for sensor information and computational resources. Intelligent agents are the buyers and sellers in this market, and they represent all the elements of the sensor network, from sensors to sensor platforms to computational resources. These agents interact based on a negotiation mechanism that determines their bidding strategies. This negotiation mechanism and the agents' bidding strategies are based on game theory, and they are designed so that the aggregate result of the multi-agent negotiation process is a market in competitive equilibrium, which guarantees an optimal allocation of resources throughout the sensor network. This paper makes two contributions to the field of market-based optimization: First, we develop a market protocol to handle heterogeneous goods in a dynamic setting. Second, we develop arbitrage agents to improve the efficiency in the market in light of its dynamic nature.

Building SDN-Based Agricultural Vehicular Sensor Networks Based on Extended Open vSwitch.

PubMed

Huang, Tao; Yan, Siyu; Yang, Fan; Pan, Tian; Liu, Jiang

2016-01-19

Software-defined vehicular sensor networks in agriculture, such as autonomous vehicle navigation based on wireless multi-sensor networks, can lead to more efficient precision agriculture. In SDN-based vehicle sensor networks, the data plane is simplified and becomes more efficient by introducing a centralized controller. However, in a wireless environment, the main controller node may leave the sensor network due to the dynamic topology change or the unstable wireless signal, leaving the rest of network devices without control, e.g., a sensor node as a switch may forward packets according to stale rules until the controller updates the flow table entries. To solve this problem, this paper proposes a novel SDN-based vehicular sensor networks architecture which can minimize the performance penalty of controller connection loss. We achieve this by designing a connection state detection and self-learning mechanism. We build prototypes based on extended Open vSwitch and Ryu. The experimental results show that the recovery time from controller connection loss is under 100 ms and it keeps rule updating in real time with a stable throughput. This architecture enhances the survivability and stability of SDN-based vehicular sensor networks in precision agriculture.

Building SDN-Based Agricultural Vehicular Sensor Networks Based on Extended Open vSwitch

PubMed Central

Huang, Tao; Yan, Siyu; Yang, Fan; Pan, Tian; Liu, Jiang

2016-01-01

Software-defined vehicular sensor networks in agriculture, such as autonomous vehicle navigation based on wireless multi-sensor networks, can lead to more efficient precision agriculture. In SDN-based vehicle sensor networks, the data plane is simplified and becomes more efficient by introducing a centralized controller. However, in a wireless environment, the main controller node may leave the sensor network due to the dynamic topology change or the unstable wireless signal, leaving the rest of network devices without control, e.g., a sensor node as a switch may forward packets according to stale rules until the controller updates the flow table entries. To solve this problem, this paper proposes a novel SDN-based vehicular sensor networks architecture which can minimize the performance penalty of controller connection loss. We achieve this by designing a connection state detection and self-learning mechanism. We build prototypes based on extended Open vSwitch and Ryu. The experimental results show that the recovery time from controller connection loss is under 100 ms and it keeps rule updating in real time with a stable throughput. This architecture enhances the survivability and stability of SDN-based vehicular sensor networks in precision agriculture. PMID:26797616

The Optimization Based Dynamic and Cyclic Working Strategies for Rechargeable Wireless Sensor Networks with Multiple Base Stations and Wireless Energy Transfer Devices

PubMed Central

Ding, Xu; Han, Jianghong; Shi, Lei

2015-01-01

In this paper, the optimal working schemes for wireless sensor networks with multiple base stations and wireless energy transfer devices are proposed. The wireless energy transfer devices also work as data gatherers while charging sensor nodes. The wireless sensor network is firstly divided into sub networks according to the concept of Voronoi diagram. Then, the entire energy replenishing procedure is split into the pre-normal and normal energy replenishing stages. With the objective of maximizing the sojourn time ratio of the wireless energy transfer device, a continuous time optimization problem for the normal energy replenishing cycle is formed according to constraints with which sensor nodes and wireless energy transfer devices should comply. Later on, the continuous time optimization problem is reshaped into a discrete multi-phased optimization problem, which yields the identical optimality. After linearizing it, we obtain a linear programming problem that can be solved efficiently. The working strategies of both sensor nodes and wireless energy transfer devices in the pre-normal replenishing stage are also discussed in this paper. The intensive simulations exhibit the dynamic and cyclic working schemes for the entire energy replenishing procedure. Additionally, a way of eliminating “bottleneck” sensor nodes is also developed in this paper. PMID:25785305

The optimization based dynamic and cyclic working strategies for rechargeable wireless sensor networks with multiple base stations and wireless energy transfer devices.

PubMed

Ding, Xu; Han, Jianghong; Shi, Lei

2015-03-16

In this paper, the optimal working schemes for wireless sensor networks with multiple base stations and wireless energy transfer devices are proposed. The wireless energy transfer devices also work as data gatherers while charging sensor nodes. The wireless sensor network is firstly divided into sub networks according to the concept of Voronoi diagram. Then, the entire energy replenishing procedure is split into the pre-normal and normal energy replenishing stages. With the objective of maximizing the sojourn time ratio of the wireless energy transfer device, a continuous time optimization problem for the normal energy replenishing cycle is formed according to constraints with which sensor nodes and wireless energy transfer devices should comply. Later on, the continuous time optimization problem is reshaped into a discrete multi-phased optimization problem, which yields the identical optimality. After linearizing it, we obtain a linear programming problem that can be solved efficiently. The working strategies of both sensor nodes and wireless energy transfer devices in the pre-normal replenishing stage are also discussed in this paper. The intensive simulations exhibit the dynamic and cyclic working schemes for the entire energy replenishing procedure. Additionally, a way of eliminating "bottleneck" sensor nodes is also developed in this paper.

Revealing networks from dynamics: an introduction

NASA Astrophysics Data System (ADS)

Timme, Marc; Casadiego, Jose

2014-08-01

What can we learn from the collective dynamics of a complex network about its interaction topology? Taking the perspective from nonlinear dynamics, we briefly review recent progress on how to infer structural connectivity (direct interactions) from accessing the dynamics of the units. Potential applications range from interaction networks in physics, to chemical and metabolic reactions, protein and gene regulatory networks as well as neural circuits in biology and electric power grids or wireless sensor networks in engineering. Moreover, we briefly mention some standard ways of inferring effective or functional connectivity.

Detecting the Influence of Spreading in Social Networks with Excitable Sensor Networks

PubMed Central

Pei, Sen; Tang, Shaoting; Zheng, Zhiming

2015-01-01

Detecting spreading outbreaks in social networks with sensors is of great significance in applications. Inspired by the formation mechanism of humans’ physical sensations to external stimuli, we propose a new method to detect the influence of spreading by constructing excitable sensor networks. Exploiting the amplifying effect of excitable sensor networks, our method can better detect small-scale spreading processes. At the same time, it can also distinguish large-scale diffusion instances due to the self-inhibition effect of excitable elements. Through simulations of diverse spreading dynamics on typical real-world social networks (Facebook, coauthor, and email social networks), we find that the excitable sensor networks are capable of detecting and ranking spreading processes in a much wider range of influence than other commonly used sensor placement methods, such as random, targeted, acquaintance and distance strategies. In addition, we validate the efficacy of our method with diffusion data from a real-world online social system, Twitter. We find that our method can detect more spreading topics in practice. Our approach provides a new direction in spreading detection and should be useful for designing effective detection methods. PMID:25950181

Increasing the Lifetime of Mobile WSNs via Dynamic Optimization of Sensor Node Communication Activity

PubMed Central

Guimarães, Dayan Adionel; Sakai, Lucas Jun; Alberti, Antonio Marcos; de Souza, Rausley Adriano Amaral

2016-01-01

In this paper, a simple and flexible method for increasing the lifetime of fixed or mobile wireless sensor networks is proposed. Based on past residual energy information reported by the sensor nodes, the sink node or another central node dynamically optimizes the communication activity levels of the sensor nodes to save energy without sacrificing the data throughput. The activity levels are defined to represent portions of time or time-frequency slots in a frame, during which the sensor nodes are scheduled to communicate with the sink node to report sensory measurements. Besides node mobility, it is considered that sensors’ batteries may be recharged via a wireless power transmission or equivalent energy harvesting scheme, bringing to the optimization problem an even more dynamic character. We report large increased lifetimes over the non-optimized network and comparable or even larger lifetime improvements with respect to an idealized greedy algorithm that uses both the real-time channel state and the residual energy information. PMID:27657075

A High-Resolution Sensor Network for Monitoring Glacier Dynamics

NASA Astrophysics Data System (ADS)

Edwards, S.; Murray, T.; O'Farrell, T.; Rutt, I. C.; Loskot, P.; Martin, I.; Selmes, N.; Aspey, R.; James, T.; Bevan, S. L.; Baugé, T.

2013-12-01

Changes in Greenland and Antarctic ice sheets due to ice flow/ice-berg calving are a major uncertainty affecting sea-level rise forecasts. Latterly GNSS (Global Navigation Satellite Systems) have been employed extensively to monitor such glacier dynamics. Until recently however, the favoured methodology has been to deploy sensors onto the glacier surface, collect data for a period of time, then retrieve and download the sensors. This approach works well in less dynamic environments where the risk of sensor loss is low. In more extreme environments e.g. approaching the glacial calving front, the risk of sensor loss and hence data loss increases dramatically. In order to provide glaciologists with new insights into flow dynamics and calving processes we have developed a novel sensor network to increase the robustness of data capture. We present details of the technological requirements for an in-situ Zigbee wireless streaming network infrastructure supporting instantaneous data acquisition from high resolution GNSS sensors thereby increasing data capture robustness. The data obtained offers new opportunities to investigate the interdependence of mass flow, uplift, velocity and geometry and the network architecture has been specifically designed for deployment by helicopter close to the calving front to yield unprecedented detailed information. Following successful field trials of a pilot three node network during 2012, a larger 20 node network was deployed on the fast-flowing Helheim glacier, south-east Greenland over the summer months of 2013. The utilisation of dual wireless transceivers in each glacier node, multiple frequencies and four ';collector' stations located on the valley sides creates overlapping networks providing enhanced capacity, diversity and redundancy of data 'back-haul', even close to ';floor' RSSI (Received Signal Strength Indication) levels around -100 dBm. Data loss through radio packet collisions within sub-networks are avoided through the adoption of beacon based time division multiple access (tdma). In-house single-epoch GNSS processing software provides 1-2 cm coordinate time-series capable of detecting a major calving event during the 2012 pilot study. These data can be synthesised with other remotely sensed data e.g. airborne lidar, oblique photogrammetry and TanDEM-X satellite imagery derived DEMs giving an opportunity to fine-tune glacial models delivering a deeper understanding of the contribution to sea-level rise made by tidewater glaciers such as Helheim. The flexibility of our network would make it suitable for deployment in other extreme environments such as areas at risk from earthquakes and landslides.

Design and Analysis of a Data Fusion Scheme in Mobile Wireless Sensor Networks Based on Multi-Protocol Mobile Agents

PubMed Central

Wu, Chunxue; Wu, Wenliang; Wan, Caihua

2017-01-01

Sensors are increasingly used in mobile environments with wireless network connections. Multiple sensor types measure distinct aspects of the same event. Their measurements are then combined to produce integrated, reliable results. As the number of sensors in networks increases, low energy requirements and changing network connections complicate event detection and measurement. We present a data fusion scheme for use in mobile wireless sensor networks with high energy efficiency and low network delays, that still produces reliable results. In the first phase, we used a network simulation where mobile agents dynamically select the next hop migration node based on the stability parameter of the link, and perform the data fusion at the migration node. Agents use the fusion results to decide if it should return the fusion results to the processing center or continue to collect more data. In the second phase. The feasibility of data fusion at the node level is confirmed by an experimental design where fused data from color sensors show near-identical results to actual physical temperatures. These results are potentially important for new large-scale sensor network applications. PMID:29099793

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition.

PubMed

Ordóñez, Francisco Javier; Roggen, Daniel

2016-01-18

Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters' influence on performance to provide insights about their optimisation.

Dynamic Reconfiguration of a RGBD Sensor Based on QoS and QoC Requirements in Distributed Systems.

PubMed

Munera, Eduardo; Poza-Lujan, Jose-Luis; Posadas-Yagüe, Juan-Luis; Simó-Ten, José-Enrique; Noguera, Juan Fco Blanes

2015-07-24

The inclusion of embedded sensors into a networked system provides useful information for many applications. A Distributed Control System (DCS) is one of the clearest examples where processing and communications are constrained by the client's requirements and the capacity of the system. An embedded sensor with advanced processing and communications capabilities supplies high level information, abstracting from the data acquisition process and objects recognition mechanisms. The implementation of an embedded sensor/actuator as a Smart Resource permits clients to access sensor information through distributed network services. Smart resources can offer sensor services as well as computing, communications and peripheral access by implementing a self-aware based adaptation mechanism which adapts the execution profile to the context. On the other hand, information integrity must be ensured when computing processes are dynamically adapted. Therefore, the processing must be adapted to perform tasks in a certain lapse of time but always ensuring a minimum process quality. In the same way, communications must try to reduce the data traffic without excluding relevant information. The main objective of the paper is to present a dynamic configuration mechanism to adapt the sensor processing and communication to the client's requirements in the DCS. This paper describes an implementation of a smart resource based on a Red, Green, Blue, and Depth (RGBD) sensor in order to test the dynamic configuration mechanism presented.

Technologies for network-centric C4ISR

NASA Astrophysics Data System (ADS)

Dunkelberger, Kirk A.

2003-07-01

Three technologies form the heart of any network-centric command, control, communication, intelligence, surveillance, and reconnaissance (C4ISR) system: distributed processing, reconfigurable networking, and distributed resource management. Distributed processing, enabled by automated federation, mobile code, intelligent process allocation, dynamic multiprocessing groups, check pointing, and other capabilities creates a virtual peer-to-peer computing network across the force. Reconfigurable networking, consisting of content-based information exchange, dynamic ad-hoc routing, information operations (perception management) and other component technologies forms the interconnect fabric for fault tolerant inter processor and node communication. Distributed resource management, which provides the means for distributed cooperative sensor management, foe sensor utilization, opportunistic collection, symbiotic inductive/deductive reasoning and other applications provides the canonical algorithms for network-centric enterprises and warfare. This paper introduces these three core technologies and briefly discusses a sampling of their component technologies and their individual contributions to network-centric enterprises and warfare. Based on the implied requirements, two new algorithms are defined and characterized which provide critical building blocks for network centricity: distributed asynchronous auctioning and predictive dynamic source routing. The first provides a reliable, efficient, effective approach for near-optimal assignment problems; the algorithm has been demonstrated to be a viable implementation for ad-hoc command and control, object/sensor pairing, and weapon/target assignment. The second is founded on traditional dynamic source routing (from mobile ad-hoc networking), but leverages the results of ad-hoc command and control (from the contributed auctioning algorithm) into significant increases in connection reliability through forward prediction. Emphasis is placed on the advantages gained from the closed-loop interaction of the multiple technologies in the network-centric application environment.

A coatable, light-weight, fast-response nanocomposite sensor for the in situ acquisition of dynamic elastic disturbance: from structural vibration to ultrasonic waves

NASA Astrophysics Data System (ADS)

Zeng, Zhihui; Liu, Menglong; Xu, Hao; Liu, Weijian; Liao, Yaozhong; Jin, Hao; Zhou, Limin; Zhang, Zhong; Su, Zhongqing

2016-06-01

Inspired by an innovative sensing philosophy, a light-weight nanocomposite sensor made of a hybrid of carbon black (CB)/polyvinylidene fluoride (PVDF) has been developed. The nanoscalar architecture and percolation characteristics of the hybrid were optimized in order to fulfil the in situ acquisition of dynamic elastic disturbance from low-frequency vibration to high-frequency ultrasonic waves. Dynamic particulate motion induced by elastic disturbance modulates the infrastructure of the CB conductive network in the sensor, with the introduction of the tunneling effect, leading to dynamic alteration in the piezoresistivity measured by the sensor. Electrical analysis, morphological characterization, and static/dynamic electromechanical response interrogation were implemented to advance our insight into the sensing mechanism of the sensor, and meanwhile facilitate understanding of the optimal percolation threshold. At the optimal threshold (˜6.5 wt%), the sensor exhibits high fidelity, a fast response, and high sensitivity to ultrafast elastic disturbance (in an ultrasonic regime up to 400 kHz), yet with an ultralow magnitude (on the order of micrometers). The performance of the sensor was evaluated against a conventional strain gauge and piezoelectric transducer, showing excellent coincidence, yet a much greater gauge factor and frequency-independent piezoresistive behavior. Coatable on a structure and deployable in a large quantity to form a dense sensor network, this nanocomposite sensor has blazed a trail for implementing in situ sensing for vibration- or ultrasonic-wave-based structural health monitoring, by striking a compromise between ‘sensing cost’ and ‘sensing effectiveness’.

A controllable sensor management algorithm capable of learning

NASA Astrophysics Data System (ADS)

Osadciw, Lisa A.; Veeramacheneni, Kalyan K.

2005-03-01

Sensor management technology progress is challenged by the geographic space it spans, the heterogeneity of the sensors, and the real-time timeframes within which plans controlling the assets are executed. This paper presents a new sensor management paradigm and demonstrates its application in a sensor management algorithm designed for a biometric access control system. This approach consists of an artificial intelligence (AI) algorithm focused on uncertainty measures, which makes the high level decisions to reduce uncertainties and interfaces with the user, integrated cohesively with a bottom up evolutionary algorithm, which optimizes the sensor network"s operation as determined by the AI algorithm. The sensor management algorithm presented is composed of a Bayesian network, the AI algorithm component, and a swarm optimization algorithm, the evolutionary algorithm. Thus, the algorithm can change its own performance goals in real-time and will modify its own decisions based on observed measures within the sensor network. The definition of the measures as well as the Bayesian network determine the robustness of the algorithm and its utility in reacting dynamically to changes in the global system.

UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring

PubMed Central

Ammad Uddin, Mohammad; Mansour, Ali; Le Jeune, Denis; Ayaz, Mohammad; Aggoune, el-Hadi M.

2018-01-01

In this study, a crop health monitoring system is developed by using state of the art technologies including wireless sensors and Unmanned Aerial Vehicles (UAVs). Conventionally data is collected from sensor nodes either by fixed base stations or mobile sinks. Mobile sinks are considered a better choice nowadays due to their improved network coverage and energy utilization. Usually, the mobile sink is used in two ways: either it goes for random walk to find the scattered nodes and collect data, or follows a pre-defined path established by the ground network/clusters. Neither of these options is suitable in our scenario due to the factors like dynamic data collection, the strict targeted area required to be scanned, unavailability of a large number of nodes, dynamic path of the UAV, and most importantly, none of these are known in advance. The contribution of this paper is the formation of dynamic runtime clusters of field sensors by considering the above mentioned factors. Furthermore a mechanism (Bayesian classifier) is defined to select best node as cluster head. The proposed system is validated through simulation results, lab and infield experiments using concept devices. The obtained results are encouraging, especially in terms of deployment time, energy, efficiency, throughput and ease of use. PMID:29439496

UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring.

PubMed

Uddin, Mohammad Ammad; Mansour, Ali; Jeune, Denis Le; Ayaz, Mohammad; Aggoune, El-Hadi M

2018-02-11

In this study, a crop health monitoring system is developed by using state of the art technologies including wireless sensors and Unmanned Aerial Vehicles (UAVs). Conventionally data is collected from sensor nodes either by fixed base stations or mobile sinks. Mobile sinks are considered a better choice nowadays due to their improved network coverage and energy utilization. Usually, the mobile sink is used in two ways: either it goes for random walk to find the scattered nodes and collect data, or follows a pre-defined path established by the ground network/clusters. Neither of these options is suitable in our scenario due to the factors like dynamic data collection, the strict targeted area required to be scanned, unavailability of a large number of nodes, dynamic path of the UAV, and most importantly, none of these are known in advance. The contribution of this paper is the formation of dynamic runtime clusters of field sensors by considering the above mentioned factors. Furthermore a mechanism (Bayesian classifier) is defined to select best node as cluster head. The proposed system is validated through simulation results, lab and infield experiments using concept devices. The obtained results are encouraging, especially in terms of deployment time, energy, efficiency, throughput and ease of use.

A spectral profile multiplexed FBG sensor network with application to strain measurement in a Kevlar woven fabric

NASA Astrophysics Data System (ADS)

Guo, Guodong; Hackney, Drew; Pankow, Mark; Peters, Kara

2017-04-01

A spectral profile division multiplexed fiber Bragg grating (FBG) sensor network is described in this paper. The unique spectral profile of each sensor in the network is identified as a distinct feature to be interrogated. Spectrum overlap is allowed under working conditions. Thus, a specific wavelength window does not need to be allocated to each sensor as in a wavelength division multiplexed (WDM) network. When the sensors are serially connected in the network, the spectrum output is expressed through a truncated series. To track the wavelength shift of each sensor, the identification problem is transformed to a nonlinear optimization problem, which is then solved by a modified dynamic multi-swarm particle swarm optimizer (DMS-PSO). To demonstrate the application of the developed network, a network consisting of four FBGs was integrated into a Kevlar woven fabric, which was under a quasi-static load imposed by an impactor head. Due to the substantial radial strain in the fabric, the spectrums of different FBGs were found to overlap during the loading process. With the developed interrogating method, the overlapped spectrum would be distinguished thus the wavelength shift of each sensor can be monitored.

Detecting and Jamming Dynamic Communication Networks in Anti-Access Environments

DTIC Science & Technology

2011-03-01

P.M. Pardalos, Y. Ye. and C.W. Commander (eds) V. Boginski. Sensors: Theory , Algorithms, and Applications. Springer, to appear in 2009. [21] Joseph C...Sumeetpal S. Singh. Nikolaos Kantas , Ba-Ngu Vo, Arnaud Doucet, and Robin J. Evans. Simulation-based optimal sensor scheduling with application to...observer trajectory planning. Aotomatica, 43(5):817-830, 2007. [27] Anthony Man-Cho So and Yinyu Ye. Theory of semidefinite programming for sensor network

Neural Networks as a Tool for Constructing Continuous NDVI Time Series from AVHRR and MODIS

NASA Technical Reports Server (NTRS)

Brown, Molly E.; Lary, David J.; Vrieling, Anton; Stathakis, Demetris; Mussa, Hamse

2008-01-01

The long term Advanced Very High Resolution Radiometer-Normalized Difference Vegetation Index (AVHRR-NDVI) record provides a critical historical perspective on vegetation dynamics necessary for global change research. Despite the proliferation of new sources of global, moderate resolution vegetation datasets, the remote sensing community is still struggling to create datasets derived from multiple sensors that allow the simultaneous use of spectral vegetation for time series analysis. To overcome the non-stationary aspect of NDVI, we use an artificial neural network (ANN) to map the NDVI indices from AVHRR to those from MODIS using atmospheric, surface type and sensor-specific inputs to account for the differences between the sensors. The NDVI dynamics and range of MODIS NDVI data at one degree is matched and extended through the AVHRR record. Four years of overlap between the two sensors is used to train a neural network to remove atmospheric and sensor specific effects on the AVHRR NDVI. In this paper, we present the resulting continuous dataset, its relationship to MODIS data, and a validation of the product.

Secured network sensor-based defense system

NASA Astrophysics Data System (ADS)

Wei, Sixiao; Shen, Dan; Ge, Linqiang; Yu, Wei; Blasch, Erik P.; Pham, Khanh D.; Chen, Genshe

2015-05-01

Network sensor-based defense (NSD) systems have been widely used to defend against cyber threats. Nonetheless, if the adversary finds ways to identify the location of monitor sensors, the effectiveness of NSD systems can be reduced. In this paper, we propose both temporal and spatial perturbation based defense mechanisms to secure NSD systems and make the monitor sensor invisible to the adversary. The temporal-perturbation based defense manipulates the timing information of published data so that the probability of successfully recognizing monitor sensors can be reduced. The spatial-perturbation based defense dynamically redeploys monitor sensors in the network so that the adversary cannot obtain the complete information to recognize all of the monitor sensors. We carried out experiments using real-world traffic traces to evaluate the effectiveness of our proposed defense mechanisms. Our data shows that our proposed defense mechanisms can reduce the attack accuracy of recognizing detection sensors.

Joint Energy Supply and Routing Path Selection for Rechargeable Wireless Sensor Networks.

PubMed

Tang, Liangrui; Cai, Jinqi; Yan, Jiangyu; Zhou, Zhenyu

2018-06-17

The topic of network lifetime has been attracting much research attention because of its importance in prolonging the standing operation of battery-restricted wireless sensor networks, and the rechargeable wireless sensor network has emerged as a promising solution. In this paper, we propose a joint energy supply and routing path selection algorithm to extend the network lifetime based on an initiative power supply. We develop a two-stage energy replenishment strategy to supplement the energy consumption of nodes as much as possible. Furthermore, the influence of charging factors on the selection of next-hop nodes in data routing is considered. The simulation results show that our algorithm effectively prolong the network lifetime, and different demands of network delay and energy consumption can be obtained by dynamically adjusting parameters.

Propagation Modeling and Defending of a Mobile Sensor Worm in Wireless Sensor and Actuator Networks.

PubMed

Wang, Tian; Wu, Qun; Wen, Sheng; Cai, Yiqiao; Tian, Hui; Chen, Yonghong; Wang, Baowei

2017-01-13

WSANs (Wireless Sensor and Actuator Networks) are derived from traditional wireless sensor networks by introducing mobile actuator elements. Previous studies indicated that mobile actuators can improve network performance in terms of data collection, energy supplementation, etc. However, according to our experimental simulations, the actuator's mobility also causes the sensor worm to spread faster if an attacker launches worm attacks on an actuator and compromises it successfully. Traditional worm propagation models and defense strategies did not consider the diffusion with a mobile worm carrier. To address this new problem, we first propose a microscopic mathematical model to describe the propagation dynamics of the sensor worm. Then, a two-step local defending strategy (LDS) with a mobile patcher (a mobile element which can distribute patches) is designed to recover the network. In LDS, all recovering operations are only taken in a restricted region to minimize the cost. Extensive experimental results demonstrate that our model estimations are rather accurate and consistent with the actual spreading scenario of the mobile sensor worm. Moreover, on average, the LDS outperforms other algorithms by approximately 50% in terms of the cost.

Dynamical jumping real-time fault-tolerant routing protocol for wireless sensor networks.

PubMed

Wu, Guowei; Lin, Chi; Xia, Feng; Yao, Lin; Zhang, He; Liu, Bing

2010-01-01

In time-critical wireless sensor network (WSN) applications, a high degree of reliability is commonly required. A dynamical jumping real-time fault-tolerant routing protocol (DMRF) is proposed in this paper. Each node utilizes the remaining transmission time of the data packets and the state of the forwarding candidate node set to dynamically choose the next hop. Once node failure, network congestion or void region occurs, the transmission mode will switch to jumping transmission mode, which can reduce the transmission time delay, guaranteeing the data packets to be sent to the destination node within the specified time limit. By using feedback mechanism, each node dynamically adjusts the jumping probabilities to increase the ratio of successful transmission. Simulation results show that DMRF can not only efficiently reduce the effects of failure nodes, congestion and void region, but also yield higher ratio of successful transmission, smaller transmission delay and reduced number of control packets.

Metadata behind the Interoperability of Wireless Sensor Networks

PubMed Central

Ballari, Daniela; Wachowicz, Monica; Callejo, Miguel Angel Manso

2009-01-01

Wireless Sensor Networks (WSNs) produce changes of status that are frequent, dynamic and unpredictable, and cannot be represented using a linear cause-effect approach. Consequently, a new approach is needed to handle these changes in order to support dynamic interoperability. Our approach is to introduce the notion of context as an explicit representation of changes of a WSN status inferred from metadata elements, which in turn, leads towards a decision-making process about how to maintain dynamic interoperability. This paper describes the developed context model to represent and reason over different WSN status based on four types of contexts, which have been identified as sensing, node, network and organisational contexts. The reasoning has been addressed by developing contextualising and bridges rules. As a result, we were able to demonstrate how contextualising rules have been used to reason on changes of WSN status as a first step towards maintaining dynamic interoperability. PMID:22412330

Metadata behind the Interoperability of Wireless Sensor Networks.

PubMed

Ballari, Daniela; Wachowicz, Monica; Callejo, Miguel Angel Manso

2009-01-01

Wireless Sensor Networks (WSNs) produce changes of status that are frequent, dynamic and unpredictable, and cannot be represented using a linear cause-effect approach. Consequently, a new approach is needed to handle these changes in order to support dynamic interoperability. Our approach is to introduce the notion of context as an explicit representation of changes of a WSN status inferred from metadata elements, which in turn, leads towards a decision-making process about how to maintain dynamic interoperability. This paper describes the developed context model to represent and reason over different WSN status based on four types of contexts, which have been identified as sensing, node, network and organisational contexts. The reasoning has been addressed by developing contextualising and bridges rules. As a result, we were able to demonstrate how contextualising rules have been used to reason on changes of WSN status as a first step towards maintaining dynamic interoperability.

Ocean Observatories Initiative (OOI): Status of Design, Capabilities, and Implementation

NASA Astrophysics Data System (ADS)

Brasseur, L. H.; Banahan, S.; Cowles, T.

2009-05-01

The National Science Foundation's (NSF) Ocean Observatories Initiative (OOI) will implement the construction and operation of an interactive, integrated ocean observing network. This research- driven, multi-scale network will provide the broad ocean science community with access to advanced technology to enable studies of fundamental ocean processes. The OOI will afford observations at coastal, regional, and global scales on timeframes of milliseconds to decades in support of investigations into climate variability, ocean ecosystems, biogeochemical processes, coastal ocean dynamics, circulation and mixing dynamics, fluid-rock interactions, and the sub-seafloor biosphere. The elements of the OOI include arrays of fixed and re-locatable moorings, autonomous underwater vehicles, and cabled seafloor nodes. All assets combined, the OOI network will provide data from over 45 distinct types of sensors, comprising over 800 total sensors distributed in the Pacific and Atlantic oceans. These core sensors for the OOI were determined through a formal process of science requirements development. This core sensor array will be integrated through a system-wide cyberinfrastructure allowing for remote control of instruments, adaptive sampling, and near-real time access to data. Implementation of the network will stimulate new avenues of research and the development of new infrastructure, instrumentation, and sensor technologies. The OOI is funded by the NSF and managed by the Consortium for Ocean Leadership which focuses on the science, technology, education, and outreach for an emerging network of ocean observing systems.

Diode laser absorption sensors for gas-dynamic and combustion flows

NASA Technical Reports Server (NTRS)

Allen, M. G.

1998-01-01

Recent advances in room-temperature, near-IR and visible diode laser sources for tele-communication, high-speed computer networks, and optical data storage applications are enabling a new generation of gas-dynamic and combustion-flow sensors based on laser absorption spectroscopy. In addition to conventional species concentration and density measurements, spectroscopic techniques for temperature, velocity, pressure and mass flux have been demonstrated in laboratory, industrial and technical flows. Combined with fibreoptic distribution networks and ultrasensitive detection strategies, compact and portable sensors are now appearing for a variety of applications. In many cases, the superior spectroscopic quality of the new laser sources compared with earlier cryogenic, mid-IR devices is allowing increased sensitivity of trace species measurements, high-precision spectroscopy of major gas constituents, and stable, autonomous measurement systems. The purpose of this article is to review recent progress in this field and suggest likely directions for future research and development. The various laser-source technologies are briefly reviewed as they relate to sensor applications. Basic theory for laser absorption measurements of gas-dynamic properties is reviewed and special detection strategies for the weak near-IR and visible absorption spectra are described. Typical sensor configurations are described and compared for various application scenarios, ranging from laboratory research to automated field and airborne packages. Recent applications of gas-dynamic sensors for air flows and fluxes of trace atmospheric species are presented. Applications of gas-dynamic and combustion sensors to research and development of high-speed flows aeropropulsion engines, and combustion emissions monitoring are presented in detail, along with emerging flow control systems based on these new sensors. Finally, technology in nonlinear frequency conversion, UV laser materials, room-temperature mid-IR materials and broadly tunable multisection devices is reviewed to suggest new sensor possibilities.

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

PubMed Central

Ordóñez, Francisco Javier; Roggen, Daniel

2016-01-01

Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation. PMID:26797612

Estimation and Fusion for Tracking Over Long-Haul Links Using Artificial Neural Networks

DOE PAGES

Liu, Qiang; Brigham, Katharine; Rao, Nageswara S. V.

2017-02-01

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors so that a final estimate of certain target characteristics – such as the position – is expected to possess much improved quality. In this paper, we pursue learning-based approaches for estimation and fusion of target states in longhaul sensor networks. In particular, we consider learning based on various implementations of artificial neural networks (ANNs). Finally, the joint effect of (i)more » imperfect communication condition, namely, link-level loss and delay, and (ii) computation constraints, in the form of low-quality sensor estimates, on ANN-based estimation and fusion, is investigated by means of analytical and simulation studies.« less

Estimation and Fusion for Tracking Over Long-Haul Links Using Artificial Neural Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Qiang; Brigham, Katharine; Rao, Nageswara S. V.

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors so that a final estimate of certain target characteristics – such as the position – is expected to possess much improved quality. In this paper, we pursue learning-based approaches for estimation and fusion of target states in longhaul sensor networks. In particular, we consider learning based on various implementations of artificial neural networks (ANNs). Finally, the joint effect of (i)more » imperfect communication condition, namely, link-level loss and delay, and (ii) computation constraints, in the form of low-quality sensor estimates, on ANN-based estimation and fusion, is investigated by means of analytical and simulation studies.« less

Input reconstruction of chaos sensors.

PubMed

Yu, Dongchuan; Liu, Fang; Lai, Pik-Yin

2008-06-01

Although the sensitivity of sensors can be significantly enhanced using chaotic dynamics due to its extremely sensitive dependence on initial conditions and parameters, how to reconstruct the measured signal from the distorted sensor response becomes challenging. In this paper we suggest an effective method to reconstruct the measured signal from the distorted (chaotic) response of chaos sensors. This measurement signal reconstruction method applies the neural network techniques for system structure identification and therefore does not require the precise information of the sensor's dynamics. We discuss also how to improve the robustness of reconstruction. Some examples are presented to illustrate the measurement signal reconstruction method suggested.

Motion-related resource allocation in dynamic wireless visual sensor network environments.

PubMed

Katsenou, Angeliki V; Kondi, Lisimachos P; Parsopoulos, Konstantinos E

2014-01-01

This paper investigates quality-driven cross-layer optimization for resource allocation in direct sequence code division multiple access wireless visual sensor networks. We consider a single-hop network topology, where each sensor transmits directly to a centralized control unit (CCU) that manages the available network resources. Our aim is to enable the CCU to jointly allocate the transmission power and source-channel coding rates for each node, under four different quality-driven criteria that take into consideration the varying motion characteristics of each recorded video. For this purpose, we studied two approaches with a different tradeoff of quality and complexity. The first one allocates the resources individually for each sensor, whereas the second clusters them according to the recorded level of motion. In order to address the dynamic nature of the recorded scenery and re-allocate the resources whenever it is dictated by the changes in the amount of motion in the scenery, we propose a mechanism based on the particle swarm optimization algorithm, combined with two restarting schemes that either exploit the previously determined resource allocation or conduct a rough estimation of it. Experimental simulations demonstrate the efficiency of the proposed approaches.

Intelligent approach to prognostic enhancements of diagnostic systems

NASA Astrophysics Data System (ADS)

Vachtsevanos, George; Wang, Peng; Khiripet, Noppadon; Thakker, Ash; Galie, Thomas R.

2001-07-01

This paper introduces a novel methodology to prognostics based on a dynamic wavelet neural network construct and notions from the virtual sensor area. This research has been motivated and supported by the U.S. Navy's active interest in integrating advanced diagnostic and prognostic algorithms in existing Naval digital control and monitoring systems. A rudimentary diagnostic platform is assumed to be available providing timely information about incipient or impending failure conditions. We focus on the development of a prognostic algorithm capable of predicting accurately and reliably the remaining useful lifetime of a failing machine or component. The prognostic module consists of a virtual sensor and a dynamic wavelet neural network as the predictor. The virtual sensor employs process data to map real measurements into difficult to monitor fault quantities. The prognosticator uses a dynamic wavelet neural network as a nonlinear predictor. Means to manage uncertainty and performance metrics are suggested for comparison purposes. An interface to an available shipboard Integrated Condition Assessment System is described and applications to shipboard equipment are discussed. Typical results from pump failures are presented to illustrate the effectiveness of the methodology.

Energy Efficient Real-Time Scheduling Using DPM on Mobile Sensors with a Uniform Multi-Cores

PubMed Central

Kim, Youngmin; Lee, Chan-Gun

2017-01-01

In wireless sensor networks (WSNs), sensor nodes are deployed for collecting and analyzing data. These nodes use limited energy batteries for easy deployment and low cost. The use of limited energy batteries is closely related to the lifetime of the sensor nodes when using wireless sensor networks. Efficient-energy management is important to extending the lifetime of the sensor nodes. Most effort for improving power efficiency in tiny sensor nodes has focused mainly on reducing the power consumed during data transmission. However, recent emergence of sensor nodes equipped with multi-cores strongly requires attention to be given to the problem of reducing power consumption in multi-cores. In this paper, we propose an energy efficient scheduling method for sensor nodes supporting a uniform multi-cores. We extend the proposed T-Ler plane based scheduling for global optimal scheduling of a uniform multi-cores and multi-processors to enable power management using dynamic power management. In the proposed approach, processor selection for a scheduling and mapping method between the tasks and processors is proposed to efficiently utilize dynamic power management. Experiments show the effectiveness of the proposed approach compared to other existing methods. PMID:29240695

Graphical user interface for wireless sensor networks simulator

NASA Astrophysics Data System (ADS)

Paczesny, Tomasz; Paczesny, Daniel; Weremczuk, Jerzy

2008-01-01

Wireless Sensor Networks (WSN) are currently very popular area of development. It can be suited in many applications form military through environment monitoring, healthcare, home automation and others. Those networks, when working in dynamic, ad-hoc model, need effective protocols which must differ from common computer networks algorithms. Research on those protocols would be difficult without simulation tool, because real applications often use many nodes and tests on such a big networks take much effort and costs. The paper presents Graphical User Interface (GUI) for simulator which is dedicated for WSN studies, especially in routing and data link protocols evaluation.

Propagation Modeling and Defending of a Mobile Sensor Worm in Wireless Sensor and Actuator Networks

PubMed Central

Wang, Tian; Wu, Qun; Wen, Sheng; Cai, Yiqiao; Tian, Hui; Chen, Yonghong; Wang, Baowei

2017-01-01

WSANs (Wireless Sensor and Actuator Networks) are derived from traditional wireless sensor networks by introducing mobile actuator elements. Previous studies indicated that mobile actuators can improve network performance in terms of data collection, energy supplementation, etc. However, according to our experimental simulations, the actuator’s mobility also causes the sensor worm to spread faster if an attacker launches worm attacks on an actuator and compromises it successfully. Traditional worm propagation models and defense strategies did not consider the diffusion with a mobile worm carrier. To address this new problem, we first propose a microscopic mathematical model to describe the propagation dynamics of the sensor worm. Then, a two-step local defending strategy (LDS) with a mobile patcher (a mobile element which can distribute patches) is designed to recover the network. In LDS, all recovering operations are only taken in a restricted region to minimize the cost. Extensive experimental results demonstrate that our model estimations are rather accurate and consistent with the actual spreading scenario of the mobile sensor worm. Moreover, on average, the LDS outperforms other algorithms by approximately 50% in terms of the cost. PMID:28098748

An Adaptive Channel Access Method for Dynamic Super Dense Wireless Sensor Networks.

PubMed

Lei, Chunyang; Bie, Hongxia; Fang, Gengfa; Zhang, Xuekun

2015-12-03

Super dense and distributed wireless sensor networks have become very popular with the development of small cell technology, Internet of Things (IoT), Machine-to-Machine (M2M) communications, Vehicular-to-Vehicular (V2V) communications and public safety networks. While densely deployed wireless networks provide one of the most important and sustainable solutions to improve the accuracy of sensing and spectral efficiency, a new channel access scheme needs to be designed to solve the channel congestion problem introduced by the high dynamics of competing nodes accessing the channel simultaneously. In this paper, we firstly analyzed the channel contention problem using a novel normalized channel contention analysis model which provides information on how to tune the contention window according to the state of channel contention. We then proposed an adaptive channel contention window tuning algorithm in which the contention window tuning rate is set dynamically based on the estimated channel contention level. Simulation results show that our proposed adaptive channel access algorithm based on fast contention window tuning can achieve more than 95 % of the theoretical optimal throughput and 0 . 97 of fairness index especially in dynamic and dense networks.

Evaluating a Novel Cellular Automata-Based Distributed Power Management Approach for Mobile Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Adabi, Sepideh; Adabi, Sahar; Rezaee, Ali

According to the traditional definition of Wireless Sensor Networks (WSNs), static sensors have limited the feasibility of WSNs in some kind of approaches, so the mobility was introduced in WSN. Mobile nodes in a WSN come equipped with battery and from the point of deployment, this battery reserve becomes a valuable resource since it cannot be replenished. Hence, maximizing the network lifetime by minimizing the energy is an important challenge in Mobile WSN. Energy conservation can be accomplished by different approaches. In this paper, we presented an energy conservation solution based on Cellular Automata. The main objective of this solution is based on dynamically adjusting the transmission range and switching between operational states of the sensor nodes.

A Multi-Objective Partition Method for Marine Sensor Networks Based on Degree of Event Correlation.

PubMed

Huang, Dongmei; Xu, Chenyixuan; Zhao, Danfeng; Song, Wei; He, Qi

2017-09-21

Existing marine sensor networks acquire data from sea areas that are geographically divided, and store the data independently in their affiliated sea area data centers. In the case of marine events across multiple sea areas, the current network structure needs to retrieve data from multiple data centers, and thus severely affects real-time decision making. In this study, in order to provide a fast data retrieval service for a marine sensor network, we use all the marine sensors as the vertices, establish the edge based on marine events, and abstract the marine sensor network as a graph. Then, we construct a multi-objective balanced partition method to partition the abstract graph into multiple regions and store them in the cloud computing platform. This method effectively increases the correlation of the sensors and decreases the retrieval cost. On this basis, an incremental optimization strategy is designed to dynamically optimize existing partitions when new sensors are added into the network. Experimental results show that the proposed method can achieve the optimal layout for distributed storage in the process of disaster data retrieval in the China Sea area, and effectively optimize the result of partitions when new buoys are deployed, which eventually will provide efficient data access service for marine events.

A genetically-encoded chloride and pH sensor for dissociating ion dynamics in the nervous system

PubMed Central

Raimondo, Joseph V.; Joyce, Bradley; Kay, Louise; Schlagheck, Theresa; Newey, Sarah E.; Srinivas, Shankar; Akerman, Colin J.

2013-01-01

Within the nervous system, intracellular Cl− and pH regulate fundamental processes including cell proliferation, metabolism, synaptic transmission, and network excitability. Cl− and pH are often co-regulated, and network activity results in the movement of both Cl− and H+. Tools to accurately measure these ions are crucial for understanding their role under physiological and pathological conditions. Although genetically-encoded Cl− and pH sensors have been described previously, these either lack ion specificity or are unsuitable for neuronal use. Here we present ClopHensorN—a new genetically-encoded ratiometric Cl− and pH sensor that is optimized for the nervous system. We demonstrate the ability of ClopHensorN to dissociate and simultaneously quantify Cl− and H+ concentrations under a variety of conditions. In addition, we establish the sensor's utility by characterizing activity-dependent ion dynamics in hippocampal neurons. PMID:24312004

A genetically-encoded chloride and pH sensor for dissociating ion dynamics in the nervous system.

PubMed

Raimondo, Joseph V; Joyce, Bradley; Kay, Louise; Schlagheck, Theresa; Newey, Sarah E; Srinivas, Shankar; Akerman, Colin J

2013-01-01

Within the nervous system, intracellular Cl(-) and pH regulate fundamental processes including cell proliferation, metabolism, synaptic transmission, and network excitability. Cl(-) and pH are often co-regulated, and network activity results in the movement of both Cl(-) and H(+). Tools to accurately measure these ions are crucial for understanding their role under physiological and pathological conditions. Although genetically-encoded Cl(-) and pH sensors have been described previously, these either lack ion specificity or are unsuitable for neuronal use. Here we present ClopHensorN-a new genetically-encoded ratiometric Cl(-) and pH sensor that is optimized for the nervous system. We demonstrate the ability of ClopHensorN to dissociate and simultaneously quantify Cl(-) and H(+) concentrations under a variety of conditions. In addition, we establish the sensor's utility by characterizing activity-dependent ion dynamics in hippocampal neurons.

Dynamic Sensor Networks

DTIC Science & Technology

2004-03-01

turned off. SLEEP Set the timer for 30 seconds before scheduled transmit time, then sleep the processor. WAKE When timer trips, power up the processor...slots where none of its neighbors are schedule to transmit. This allows the sensor nodes to perform a simple power man- agement scheme that puts the...routing This simple case study highlights the following crucial observation: optimal traffic scheduling in energy constrained networks requires future

Spatio-Temporal Constrained Human Trajectory Generation from the PIR Motion Detector Sensor Network Data: A Geometric Algebra Approach

PubMed Central

Yu, Zhaoyuan; Yuan, Linwang; Luo, Wen; Feng, Linyao; Lv, Guonian

2015-01-01

Passive infrared (PIR) motion detectors, which can support long-term continuous observation, are widely used for human motion analysis. Extracting all possible trajectories from the PIR sensor networks is important. Because the PIR sensor does not log location and individual information, none of the existing methods can generate all possible human motion trajectories that satisfy various spatio-temporal constraints from the sensor activation log data. In this paper, a geometric algebra (GA)-based approach is developed to generate all possible human trajectories from the PIR sensor network data. Firstly, the representation of the geographical network, sensor activation response sequences and the human motion are represented as algebraic elements using GA. The human motion status of each sensor activation are labeled using the GA-based trajectory tracking. Then, a matrix multiplication approach is developed to dynamically generate the human trajectories according to the sensor activation log and the spatio-temporal constraints. The method is tested with the MERL motion database. Experiments show that our method can flexibly extract the major statistical pattern of the human motion. Compared with direct statistical analysis and tracklet graph method, our method can effectively extract all possible trajectories of the human motion, which makes it more accurate. Our method is also likely to provides a new way to filter other passive sensor log data in sensor networks. PMID:26729123

Spatio-Temporal Constrained Human Trajectory Generation from the PIR Motion Detector Sensor Network Data: A Geometric Algebra Approach.

PubMed

Yu, Zhaoyuan; Yuan, Linwang; Luo, Wen; Feng, Linyao; Lv, Guonian

2015-12-30

Passive infrared (PIR) motion detectors, which can support long-term continuous observation, are widely used for human motion analysis. Extracting all possible trajectories from the PIR sensor networks is important. Because the PIR sensor does not log location and individual information, none of the existing methods can generate all possible human motion trajectories that satisfy various spatio-temporal constraints from the sensor activation log data. In this paper, a geometric algebra (GA)-based approach is developed to generate all possible human trajectories from the PIR sensor network data. Firstly, the representation of the geographical network, sensor activation response sequences and the human motion are represented as algebraic elements using GA. The human motion status of each sensor activation are labeled using the GA-based trajectory tracking. Then, a matrix multiplication approach is developed to dynamically generate the human trajectories according to the sensor activation log and the spatio-temporal constraints. The method is tested with the MERL motion database. Experiments show that our method can flexibly extract the major statistical pattern of the human motion. Compared with direct statistical analysis and tracklet graph method, our method can effectively extract all possible trajectories of the human motion, which makes it more accurate. Our method is also likely to provides a new way to filter other passive sensor log data in sensor networks.

Towards an integrated defense system for cyber security situation awareness experiment

NASA Astrophysics Data System (ADS)

Zhang, Hanlin; Wei, Sixiao; Ge, Linqiang; Shen, Dan; Yu, Wei; Blasch, Erik P.; Pham, Khanh D.; Chen, Genshe

2015-05-01

In this paper, an implemented defense system is demonstrated to carry out cyber security situation awareness. The developed system consists of distributed passive and active network sensors designed to effectively capture suspicious information associated with cyber threats, effective detection schemes to accurately distinguish attacks, and network actors to rapidly mitigate attacks. Based on the collected data from network sensors, image-based and signals-based detection schemes are implemented to detect attacks. To further mitigate attacks, deployed dynamic firewalls on hosts dynamically update detection information reported from the detection schemes and block attacks. The experimental results show the effectiveness of the proposed system. A future plan to design an effective defense system is also discussed based on system theory.

A Method of Data Aggregation for Wearable Sensor Systems

PubMed Central

Shen, Bo; Fu, Jun-Song

2016-01-01

Data aggregation has been considered as an effective way to decrease the data to be transferred in sensor networks. Particularly for wearable sensor systems, smaller battery has less energy, which makes energy conservation in data transmission more important. Nevertheless, wearable sensor systems usually have features like frequently dynamic changes of topologies and data over a large range, of which current aggregating methods can’t adapt to the demand. In this paper, we study the system composed of many wearable devices with sensors, such as the network of a tactical unit, and introduce an energy consumption-balanced method of data aggregation, named LDA-RT. In the proposed method, we develop a query algorithm based on the idea of ‘happened-before’ to construct a dynamic and energy-balancing routing tree. We also present a distributed data aggregating and sorting algorithm to execute top-k query and decrease the data that must be transferred among wearable devices. Combining these algorithms, LDA-RT tries to balance the energy consumptions for prolonging the lifetime of wearable sensor systems. Results of evaluation indicate that LDA-RT performs well in constructing routing trees and energy balances. It also outperforms the filter-based top-k monitoring approach in energy consumption, load balance, and the network’s lifetime, especially for highly dynamic data sources. PMID:27347953

Tidal analysis of GNSS data from a high resolution sensor network at Helheim Glacier

NASA Astrophysics Data System (ADS)

Martin, Ian; Aspey, Robin; Baugé, Tim; Edwards, Stuart; Everett, Alistair; James, Timothy; Loskot, Pavel; Murray, Tavi; O'Farrell, Tim; Rutt, Ian

2014-05-01

Changes in Greenland and Antarctic ice sheets due to ice flow/ice-berg calving are a major uncertainty affecting sea-level rise forecasts. Latterly GNSS (Global Navigation Satellite Systems) have been employed extensively to monitor such glacier dynamics. Until recently however, the favoured methodology has been to deploy sensors onto the glacier surface, collect data for a period of time, then retrieve and download the sensors. This approach works well in less dynamic environments where the risk of sensor loss is low. In more extreme environments e.g. approaching the glacial calving front, the risk of sensor loss and hence data loss increases dramatically. In order to provide glaciologists with new insights into flow dynamics and calving processes we have developed a novel sensor network to increase the robustness of data capture. We present details of the technological requirements for an in-situ Zigbee wireless streaming network infrastructure supporting instantaneous data acquisition from high resolution GNSS sensors thereby increasing data capture robustness. The data obtained offers new opportunities to investigate the interdependence of mass flow, uplift, velocity and geometry and the network architecture has been specifically designed for deployment by helicopter close to the calving front to yield unprecedented detailed information. Following successful field trials of a pilot three node network during 2012, a larger 20 node network was deployed on the fast-flowing Helheim glacier, south-east Greenland over the summer months of 2013. The utilisation of dual wireless transceivers in each glacier node, multiple frequencies and four 'collector' stations located on the valley sides creates overlapping networks providing enhanced capacity, diversity and redundancy of data 'back-haul', even close to 'floor' RSSI (Received Signal Strength Indication) levels around -100 dBm. Data loss through radio packet collisions within sub-networks are avoided through the adoption of beacon based time division multiple access (tdma). The processed GNSS data provides 1-2 cm accurate coordinate time-series at 3-5 second intervals. These time series are able to capture the glaciers response to major calving events as it receded ~1.5 km and smaller diurnal and semi-diurnal variations in vertical and horizontal motion linked to tidal forcing. Using images from time lapse cameras to locate the calving events we are able to quantify the variation in tidal response over the 3km x 5km area at the calving front during the 53 day study period.

Activity recognition using dynamic multiple sensor fusion in body sensor networks.

PubMed

Gao, Lei; Bourke, Alan K; Nelson, John

2012-01-01

Multiple sensor fusion is a main research direction for activity recognition. However, there are two challenges in those systems: the energy consumption due to the wireless transmission and the classifier design because of the dynamic feature vector. This paper proposes a multi-sensor fusion framework, which consists of the sensor selection module and the hierarchical classifier. The sensor selection module adopts the convex optimization to select the sensor subset in real time. The hierarchical classifier combines the Decision Tree classifier with the Naïve Bayes classifier. The dataset collected from 8 subjects, who performed 8 scenario activities, was used to evaluate the proposed system. The results show that the proposed system can obviously reduce the energy consumption while guaranteeing the recognition accuracy.

An Efficient and Adaptive Mutual Authentication Framework for Heterogeneous Wireless Sensor Network-Based Applications

PubMed Central

Kumar, Pardeep; Ylianttila, Mika; Gurtov, Andrei; Lee, Sang-Gon; Lee, Hoon-Jae

2014-01-01

Robust security is highly coveted in real wireless sensor network (WSN) applications since wireless sensors' sense critical data from the application environment. This article presents an efficient and adaptive mutual authentication framework that suits real heterogeneous WSN-based applications (such as smart homes, industrial environments, smart grids, and healthcare monitoring). The proposed framework offers: (i) key initialization; (ii) secure network (cluster) formation (i.e., mutual authentication and dynamic key establishment); (iii) key revocation; and (iv) new node addition into the network. The correctness of the proposed scheme is formally verified. An extensive analysis shows the proposed scheme coupled with message confidentiality, mutual authentication and dynamic session key establishment, node privacy, and message freshness. Moreover, the preliminary study also reveals the proposed framework is secure against popular types of attacks, such as impersonation attacks, man-in-the-middle attacks, replay attacks, and information-leakage attacks. As a result, we believe the proposed framework achieves efficiency at reasonable computation and communication costs and it can be a safeguard to real heterogeneous WSN applications. PMID:24521942

An efficient and adaptive mutual authentication framework for heterogeneous wireless sensor network-based applications.

PubMed

Kumar, Pardeep; Ylianttila, Mika; Gurtov, Andrei; Lee, Sang-Gon; Lee, Hoon-Jae

2014-02-11

Robust security is highly coveted in real wireless sensor network (WSN) applications since wireless sensors' sense critical data from the application environment. This article presents an efficient and adaptive mutual authentication framework that suits real heterogeneous WSN-based applications (such as smart homes, industrial environments, smart grids, and healthcare monitoring). The proposed framework offers: (i) key initialization; (ii) secure network (cluster) formation (i.e., mutual authentication and dynamic key establishment); (iii) key revocation; and (iv) new node addition into the network. The correctness of the proposed scheme is formally verified. An extensive analysis shows the proposed scheme coupled with message confidentiality, mutual authentication and dynamic session key establishment, node privacy, and message freshness. Moreover, the preliminary study also reveals the proposed framework is secure against popular types of attacks, such as impersonation attacks, man-in-the-middle attacks, replay attacks, and information-leakage attacks. As a result, we believe the proposed framework achieves efficiency at reasonable computation and communication costs and it can be a safeguard to real heterogeneous WSN applications.

Assessing Routing Strategies for Cognitive Radio Sensor Networks

PubMed Central

Zubair, Suleiman; Fisal, Norsheila; Baguda, Yakubu S.; Saleem, Kashif

2013-01-01

Interest in the cognitive radio sensor network (CRSN) paradigm has gradually grown among researchers. This concept seeks to fuse the benefits of dynamic spectrum access into the sensor network, making it a potential player in the next generation (NextGen) network, which is characterized by ubiquity. Notwithstanding its massive potential, little research activity has been dedicated to the network layer. By contrast, we find recent research trends focusing on the physical layer, the link layer and the transport layers. The fact that the cross-layer approach is imperative, due to the resource-constrained nature of CRSNs, can make the design of unique solutions non-trivial in this respect. This paper seeks to explore possible design opportunities with wireless sensor networks (WSNs), cognitive radio ad-hoc networks (CRAHNs) and cross-layer considerations for implementing viable CRSN routing solutions. Additionally, a detailed performance evaluation of WSN routing strategies in a cognitive radio environment is performed to expose research gaps. With this work, we intend to lay a foundation for developing CRSN routing solutions and to establish a basis for future work in this area. PMID:24077319

Dynamic Task Allocation in Multi-Hop Multimedia Wireless Sensor Networks with Low Mobility

PubMed Central

Jin, Yichao; Vural, Serdar; Gluhak, Alexander; Moessner, Klaus

2013-01-01

This paper presents a task allocation-oriented framework to enable efficient in-network processing and cost-effective multi-hop resource sharing for dynamic multi-hop multimedia wireless sensor networks with low node mobility, e.g., pedestrian speeds. The proposed system incorporates a fast task reallocation algorithm to quickly recover from possible network service disruptions, such as node or link failures. An evolutional self-learning mechanism based on a genetic algorithm continuously adapts the system parameters in order to meet the desired application delay requirements, while also achieving a sufficiently long network lifetime. Since the algorithm runtime incurs considerable time delay while updating task assignments, we introduce an adaptive window size to limit the delay periods and ensure an up-to-date solution based on node mobility patterns and device processing capabilities. To the best of our knowledge, this is the first study that yields multi-objective task allocation in a mobile multi-hop wireless environment under dynamic conditions. Simulations are performed in various settings, and the results show considerable performance improvement in extending network lifetime compared to heuristic mechanisms. Furthermore, the proposed framework provides noticeable reduction in the frequency of missing application deadlines. PMID:24135992

Experimental and Computational Fluid Dynamic Analysis of Axial-Flow Hydrodynamic Power Turbine

DTIC Science & Technology

2013-03-01

Number RPM Revolutions per minute WSN Wireless Sensor Network xvi THIS PAGE INTENTIONALLY LEFT BLANK xvii ACKNOWLEDGMENTS I would like...Instruments Wireless Sensor Network (WSN) device, strain data could be sent to Labview acquisition software during a run across the tank. Four channels...be more appropriate for automobiles where minimizing drag is an important design aspect. Conversely, drag coefficients for wind turbine rotors are

Polyaniline nanowires-gold nanoparticles hybrid network based chemiresistive hydrogen sulfide sensor

NASA Astrophysics Data System (ADS)

Shirsat, Mahendra D.; Bangar, Mangesh A.; Deshusses, Marc A.; Myung, Nosang V.; Mulchandani, Ashok

2009-02-01

We report a sensitive, selective, and fast responding room temperature chemiresistive sensor for hydrogen sulfide detection and quantification using polyaniline nanowires-gold nanoparticles hybrid network. The sensor was fabricated by facile electrochemical technique. Initially, polyaniline nanowires with a diameter of 250-320 nm bridging the gap between a pair of microfabricated gold electrodes were synthesized using templateless electrochemical polymerization using a two step galvanostatic technique. Polyaniline nanowires were then electrochemically functionalized with gold nanoparticles using cyclic voltammetry technique. These chemiresistive sensors show an excellent limit of detection (0.1 ppb), wide dynamic range (0.1-100 ppb), and very good selectivity and reproducibility.

Efficient Usage of Dense GNSS Networks in Central Europe for the Visualization and Investigation of Ionospheric TEC Variations

PubMed Central

Zanimonskiy, Yevgen M.; Yampolski, Yuri M.; Figurski, Mariusz

2017-01-01

The technique of the orthogonal projection of ionosphere electronic content variations for mapping total electron content (TEC) allows us to visualize ionospheric irregularities. For the reconstruction of global ionospheric characteristics, numerous global navigation satellite system (GNSS) receivers located in different regions of the Earth are used as sensors. We used dense GNSS networks in central Europe to detect and investigate a special type of plasma inhomogeneities, called travelling ionospheric disturbances (TID). Such use of GNSS sensors allows us to reconstruct the main TID parameters, such as spatial dimensions, velocities, and directions of their movement. The paper gives examples of the restoration of dynamic characteristics of ionospheric irregularities for quiet and disturbed geophysical conditions. Special attention is paid to the dynamics of ionospheric disturbances stimulated by the magnetic storms of two St. Patrick’s Days (17 March 2013 and 2015). Additional opportunities for the remote sensing of the ionosphere with the use of dense regional networks of GNSS receiving sensors have been noted too. PMID:28994718

Efficient Usage of Dense GNSS Networks in Central Europe for the Visualization and Investigation of Ionospheric TEC Variations.

PubMed

Nykiel, Grzegorz; Zanimonskiy, Yevgen M; Yampolski, Yuri M; Figurski, Mariusz

2017-10-10

The technique of the orthogonal projection of ionosphere electronic content variations for mapping total electron content (TEC) allows us to visualize ionospheric irregularities. For the reconstruction of global ionospheric characteristics, numerous global navigation satellite system (GNSS) receivers located in different regions of the Earth are used as sensors. We used dense GNSS networks in central Europe to detect and investigate a special type of plasma inhomogeneities, called travelling ionospheric disturbances (TID). Such use of GNSS sensors allows us to reconstruct the main TID parameters, such as spatial dimensions, velocities, and directions of their movement. The paper gives examples of the restoration of dynamic characteristics of ionospheric irregularities for quiet and disturbed geophysical conditions. Special attention is paid to the dynamics of ionospheric disturbances stimulated by the magnetic storms of two St. Patrick's Days (17 March 2013 and 2015). Additional opportunities for the remote sensing of the ionosphere with the use of dense regional networks of GNSS receiving sensors have been noted too.

Distributed wireless sensing for methane leak detection technology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Klein, Levente; van Kesse, Theodor

Large scale environmental monitoring requires dynamic optimization of data transmission, power management, and distribution of the computational load. In this work, we demonstrate the use of a wireless sensor network for detection of chemical leaks on gas oil well pads. The sensor network consist of chemi-resistive and wind sensors and aggregates all the data and transmits it to the cloud for further analytics processing. The sensor network data is integrated with an inversion model to identify leak location and quantify leak rates. We characterize the sensitivity and accuracy of such system under multiple well controlled methane release experiments. It ismore » demonstrated that even 1 hour measurement with 10 sensors localizes leaks within 1 m and determines leak rate with an accuracy of 40%. This integrated sensing and analytics solution is currently refined to be a robust system for long term remote monitoring of methane leaks, generation of alarms, and tracking regulatory compliance.« less

Distributed wireless sensing for fugitive methane leak detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Klein, Levente J.; van Kessel, Theodore; Nair, Dhruv

Large scale environmental monitoring requires dynamic optimization of data transmission, power management, and distribution of the computational load. In this work, we demonstrate the use of a wireless sensor network for detection of chemical leaks on gas oil well pads. The sensor network consist of chemi-resistive and wind sensors and aggregates all the data and transmits it to the cloud for further analytics processing. The sensor network data is integrated with an inversion model to identify leak location and quantify leak rates. We characterize the sensitivity and accuracy of such system under multiple well controlled methane release experiments. It ismore » demonstrated that even 1 hour measurement with 10 sensors localizes leaks within 1 m and determines leak rate with an accuracy of 40%. This integrated sensing and analytics solution is currently refined to be a robust system for long term remote monitoring of methane leaks, generation of alarms, and tracking regulatory compliance.« less

Distributed wireless sensing for fugitive methane leak detection

DOE PAGES

Klein, Levente J.; van Kessel, Theodore; Nair, Dhruv; ...

2017-12-11

Large scale environmental monitoring requires dynamic optimization of data transmission, power management, and distribution of the computational load. In this work, we demonstrate the use of a wireless sensor network for detection of chemical leaks on gas oil well pads. The sensor network consist of chemi-resistive and wind sensors and aggregates all the data and transmits it to the cloud for further analytics processing. The sensor network data is integrated with an inversion model to identify leak location and quantify leak rates. We characterize the sensitivity and accuracy of such system under multiple well controlled methane release experiments. It ismore » demonstrated that even 1 hour measurement with 10 sensors localizes leaks within 1 m and determines leak rate with an accuracy of 40%. This integrated sensing and analytics solution is currently refined to be a robust system for long term remote monitoring of methane leaks, generation of alarms, and tracking regulatory compliance.« less

Social Sensor Analytics: Making Sense of Network Models in Social Media

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dowling, Chase P.; Harrison, Joshua J.; Sathanur, Arun V.

Social networks can be thought of as noisy sensor networks mapping real world information to the web. Owing to the extensive body of literature in sensor network analysis, this work sought to apply several novel and traditional methods in sensor network analysis for the purposes of efficiently interrogating social media data streams from raw data. We carefully revisit our definition of a social media signal from previous work both in terms of time-varying features within the data and the networked nature of the medium. Further, we detail our analysis of global patterns in Twitter over the months of November 2013more » and June 2014, detect and categorize events, and illustrate how these analyses can be used to inform graph-based models of Twitter, namely using a recent network influence model called PhySense: similar to PageRank but tuned to behavioral analysis by leveraging a sociologically inspired probabilistic model. We ultimately identify forms of information dissemination via analysis of time series and dynamic graph spectra and corroborate these findings through manual investigation of the data as a requisite step in modeling the diffusion process with PhySense. We hope to sufficiently characterize global behavior in a medium such as Twitter as a means of learning global model parameters one may use to predict or simulate behavior on a large scale. We have made our time series and dynamic graph analytical code available via a GitHub repository https://github.com/cpatdowling/salsa and our data are available upon request.« less

Information-Theoretic Performance Analysis of Sensor Networks via Markov Modeling of Time Series Data.

PubMed

Li, Yue; Jha, Devesh K; Ray, Asok; Wettergren, Thomas A; Yue Li; Jha, Devesh K; Ray, Asok; Wettergren, Thomas A; Wettergren, Thomas A; Li, Yue; Ray, Asok; Jha, Devesh K

2018-06-01

This paper presents information-theoretic performance analysis of passive sensor networks for detection of moving targets. The proposed method falls largely under the category of data-level information fusion in sensor networks. To this end, a measure of information contribution for sensors is formulated in a symbolic dynamics framework. The network information state is approximately represented as the largest principal component of the time series collected across the network. To quantify each sensor's contribution for generation of the information content, Markov machine models as well as x-Markov (pronounced as cross-Markov) machine models, conditioned on the network information state, are constructed; the difference between the conditional entropies of these machines is then treated as an approximate measure of information contribution by the respective sensors. The x-Markov models represent the conditional temporal statistics given the network information state. The proposed method has been validated on experimental data collected from a local area network of passive sensors for target detection, where the statistical characteristics of environmental disturbances are similar to those of the target signal in the sense of time scale and texture. A distinctive feature of the proposed algorithm is that the network decisions are independent of the behavior and identity of the individual sensors, which is desirable from computational perspectives. Results are presented to demonstrate the proposed method's efficacy to correctly identify the presence of a target with very low false-alarm rates. The performance of the underlying algorithm is compared with that of a recent data-driven, feature-level information fusion algorithm. It is shown that the proposed algorithm outperforms the other algorithm.

Research on FBG-Based CFRP Structural Damage Identification Using BP Neural Network

NASA Astrophysics Data System (ADS)

Geng, Xiangyi; Lu, Shizeng; Jiang, Mingshun; Sui, Qingmei; Lv, Shanshan; Xiao, Hang; Jia, Yuxi; Jia, Lei

2018-06-01

A damage identification system of carbon fiber reinforced plastics (CFRP) structures is investigated using fiber Bragg grating (FBG) sensors and back propagation (BP) neural network. FBG sensors are applied to construct the sensing network to detect the structural dynamic response signals generated by active actuation. The damage identification model is built based on the BP neural network. The dynamic signal characteristics extracted by the Fourier transform are the inputs, and the damage states are the outputs of the model. Besides, damages are simulated by placing lumped masses with different weights instead of inducing real damages, which is confirmed to be feasible by finite element analysis (FEA). At last, the damage identification system is verified on a CFRP plate with 300 mm × 300 mm experimental area, with the accurate identification of varied damage states. The system provides a practical way for CFRP structural damage identification.

A wireless sensor enabled by wireless power.

PubMed

Lee, Da-Sheng; Liu, Yu-Hong; Lin, Chii-Ruey

2012-11-22

Through harvesting energy by wireless charging and delivering data by wireless communication, this study proposes the concept of a wireless sensor enabled by wireless power (WPWS) and reports the fabrication of a prototype for functional tests. One WPWS node consists of wireless power module and sensor module with different chip-type sensors. Its main feature is the dual antenna structure. Following RFID system architecture, a power harvesting antenna was designed to gather power from a standard reader working in the 915 MHz band. Referring to the Modbus protocol, the other wireless communication antenna was integrated on a node to send sensor data in parallel. The dual antenna structure integrates both the advantages of an RFID system and a wireless sensor. Using a standard UHF RFID reader, WPWS can be enabled in a distributed area with a diameter up to 4 m. Working status is similar to that of a passive tag, except that a tag can only be queried statically, while the WPWS can send dynamic data from the sensors. The function is the same as a wireless sensor node. Different WPWSs equipped with temperature and humidity, optical and airflow velocity sensors are tested in this study. All sensors can send back detection data within 8 s. The accuracy is within 8% deviation compared with laboratory equipment. A wireless sensor network enabled by wireless power should be a totally wireless sensor network using WPWS. However, distributed WPWSs only can form a star topology, the simplest topology for constructing a sensor network. Because of shielding effects, it is difficult to apply other complex topologies. Despite this limitation, WPWS still can be used to extend sensor network applications in hazardous environments. Further research is needed to improve WPWS to realize a totally wireless sensor network.

A Wireless Sensor Enabled by Wireless Power

PubMed Central

Lee, Da-Sheng; Liu, Yu-Hong; Lin, Chii-Ruey

2012-01-01

Through harvesting energy by wireless charging and delivering data by wireless communication, this study proposes the concept of a wireless sensor enabled by wireless power (WPWS) and reports the fabrication of a prototype for functional tests. One WPWS node consists of wireless power module and sensor module with different chip-type sensors. Its main feature is the dual antenna structure. Following RFID system architecture, a power harvesting antenna was designed to gather power from a standard reader working in the 915 MHz band. Referring to the Modbus protocol, the other wireless communication antenna was integrated on a node to send sensor data in parallel. The dual antenna structure integrates both the advantages of an RFID system and a wireless sensor. Using a standard UHF RFID reader, WPWS can be enabled in a distributed area with a diameter up to 4 m. Working status is similar to that of a passive tag, except that a tag can only be queried statically, while the WPWS can send dynamic data from the sensors. The function is the same as a wireless sensor node. Different WPWSs equipped with temperature and humidity, optical and airflow velocity sensors are tested in this study. All sensors can send back detection data within 8 s. The accuracy is within 8% deviation compared with laboratory equipment. A wireless sensor network enabled by wireless power should be a totally wireless sensor network using WPWS. However, distributed WPWSs only can form a star topology, the simplest topology for constructing a sensor network. Because of shielding effects, it is difficult to apply other complex topologies. Despite this limitation, WPWS still can be used to extend sensor network applications in hazardous environments. Further research is needed to improve WPWS to realize a totally wireless sensor network. PMID:23443370

Open architecture of smart sensor suites

NASA Astrophysics Data System (ADS)

Müller, Wilmuth; Kuwertz, Achim; Grönwall, Christina; Petersson, Henrik; Dekker, Rob; Reinert, Frank; Ditzel, Maarten

2017-10-01

Experiences from recent conflicts show the strong need for smart sensor suites comprising different multi-spectral imaging sensors as core elements as well as additional non-imaging sensors. Smart sensor suites should be part of a smart sensor network - a network of sensors, databases, evaluation stations and user terminals. Its goal is to optimize the use of various information sources for military operations such as situation assessment, intelligence, surveillance, reconnaissance, target recognition and tracking. Such a smart sensor network will enable commanders to achieve higher levels of situational awareness. Within the study at hand, an open system architecture was developed in order to increase the efficiency of sensor suites. The open system architecture for smart sensor suites, based on a system-of-systems approach, enables combining different sensors in multiple physical configurations, such as distributed sensors, co-located sensors combined in a single package, tower-mounted sensors, sensors integrated in a mobile platform, and trigger sensors. The architecture was derived from a set of system requirements and relevant scenarios. Its mode of operation is adaptable to a series of scenarios with respect to relevant objects of interest, activities to be observed, available transmission bandwidth, etc. The presented open architecture is designed in accordance with the NATO Architecture Framework (NAF). The architecture allows smart sensor suites to be part of a surveillance network, linked e.g. to a sensor planning system and a C4ISR center, and to be used in combination with future RPAS (Remotely Piloted Aircraft Systems) for supporting a more flexible dynamic configuration of RPAS payloads.

Lag compensation of optical fibers or thermocouples to achieve waveform fidelity in dynamic gas pyrometry

NASA Technical Reports Server (NTRS)

Warshawsky, I.

1991-01-01

Fidelity of waveform reproduction requires constant amplitude ratio and constant time lag of a temperature sensor's indication, at all frequencies of interest. However, heat-transfer type sensors usually cannot satisfy these requirements. Equations for the actual indication of a thermocouple and an optical-fiber pyrometer are given explicitly, in terms of sensor and flowing-gas properties. A practical, realistic design of each type of sensor behaves like a first-order system with amplitude-ratio attenuation inversely proportional to frequency when the frequency exceeds the corner frequency. Only at much higher frequencies does the amplitude-ratio attenuation for the optical fiber sensor become inversely proportional to the square root of the frequency. Design options for improving the frequency response are discussed. On-line electrical lag compensation, using a linear amplifier and a passive compensation network, can extend the corner frequency of the thermocouple 100-fold or more; a similar passive network can be used for the optical-fiber sensor. Design details for these networks are presented.

Secure and Cost-Effective Distributed Aggregation for Mobile Sensor Networks

PubMed Central

Guo, Kehua; Zhang, Ping; Ma, Jianhua

2016-01-01

Secure data aggregation (SDA) schemes are widely used in distributed applications, such as mobile sensor networks, to reduce communication cost, prolong the network life cycle and provide security. However, most SDA are only suited for a single type of statistics (i.e., summation-based or comparison-based statistics) and are not applicable to obtaining multiple statistic results. Most SDA are also inefficient for dynamic networks. This paper presents multi-functional secure data aggregation (MFSDA), in which the mapping step and coding step are introduced to provide value-preserving and order-preserving and, later, to enable arbitrary statistics support in the same query. MFSDA is suited for dynamic networks because these active nodes can be counted directly from aggregation data. The proposed scheme is tolerant to many types of attacks. The network load of the proposed scheme is balanced, and no significant bottleneck exists. The MFSDA includes two versions: MFSDA-I and MFSDA-II. The first one can obtain accurate results, while the second one is a more generalized version that can significantly reduce network traffic at the expense of less accuracy loss. PMID:27120599

A Hybrid Memetic Framework for Coverage Optimization in Wireless Sensor Networks.

PubMed

Chen, Chia-Pang; Mukhopadhyay, Subhas Chandra; Chuang, Cheng-Long; Lin, Tzu-Shiang; Liao, Min-Sheng; Wang, Yung-Chung; Jiang, Joe-Air

2015-10-01

One of the critical concerns in wireless sensor networks (WSNs) is the continuous maintenance of sensing coverage. Many particular applications, such as battlefield intrusion detection and object tracking, require a full-coverage at any time, which is typically resolved by adding redundant sensor nodes. With abundant energy, previous studies suggested that the network lifetime can be maximized while maintaining full coverage through organizing sensor nodes into a maximum number of disjoint sets and alternately turning them on. Since the power of sensor nodes is unevenly consumed over time, and early failure of sensor nodes leads to coverage loss, WSNs require dynamic coverage maintenance. Thus, the task of permanently sustaining full coverage is particularly formulated as a hybrid of disjoint set covers and dynamic-coverage-maintenance problems, and both have been proven to be nondeterministic polynomial-complete. In this paper, a hybrid memetic framework for coverage optimization (Hy-MFCO) is presented to cope with the hybrid problem using two major components: 1) a memetic algorithm (MA)-based scheduling strategy and 2) a heuristic recursive algorithm (HRA). First, the MA-based scheduling strategy adopts a dynamic chromosome structure to create disjoint sets, and then the HRA is utilized to compensate the loss of coverage by awaking some of the hibernated nodes in local regions when a disjoint set fails to maintain full coverage. The results obtained from real-world experiments using a WSN test-bed and computer simulations indicate that the proposed Hy-MFCO is able to maximize sensing coverage while achieving energy efficiency at the same time. Moreover, the results also show that the Hy-MFCO significantly outperforms the existing methods with respect to coverage preservation and energy efficiency.

A Dynamic Infrastructure for Interconnecting Disparate ISR/ISTAR Assets (the ITA Sensor Fabric)

DTIC Science & Technology

2009-07-01

areas of sensor identification, classification, interoperability and sensor data sharing, dissemination and consumability. This paper presents the ITA...sensors in the area of operations. This paper also presents a use case scenario developed in partnership with the U.S. Army Research Laboratory (ARL) and... paper we describe the Fabric, and its application to a simulated representative coalition operation scenario. The Fabric spans the network from the

An empirical evaluation of lightweight random walk based routing protocol in duty cycle aware wireless sensor networks.

PubMed

Mian, Adnan Noor; Fatima, Mehwish; Khan, Raees; Prakash, Ravi

2014-01-01

Energy efficiency is an important design paradigm in Wireless Sensor Networks (WSNs) and its consumption in dynamic environment is even more critical. Duty cycling of sensor nodes is used to address the energy consumption problem. However, along with advantages, duty cycle aware networks introduce some complexities like synchronization and latency. Due to their inherent characteristics, many traditional routing protocols show low performance in densely deployed WSNs with duty cycle awareness, when sensor nodes are supposed to have high mobility. In this paper we first present a three messages exchange Lightweight Random Walk Routing (LRWR) protocol and then evaluate its performance in WSNs for routing low data rate packets. Through NS-2 based simulations, we examine the LRWR protocol by comparing it with DYMO, a widely used WSN protocol, in both static and dynamic environments with varying duty cycles, assuming the standard IEEE 802.15.4 in lower layers. Results for the three metrics, that is, reliability, end-to-end delay, and energy consumption, show that LRWR protocol outperforms DYMO in scalability, mobility, and robustness, showing this protocol as a suitable choice in low duty cycle and dense WSNs.

Converging Redundant Sensor Network Information for Improved Building Control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dale Tiller; D. Phil; Gregor Henze

2007-09-30

This project investigated the development and application of sensor networks to enhance building energy management and security. Commercial, industrial and residential buildings often incorporate systems used to determine occupancy, but current sensor technology and control algorithms limit the effectiveness of these systems. For example, most of these systems rely on single monitoring points to detect occupancy, when more than one monitoring point could improve system performance. Phase I of the project focused on instrumentation and data collection. During the initial project phase, a new occupancy detection system was developed, commissioned and installed in a sample of private offices and open-planmore » office workstations. Data acquisition systems were developed and deployed to collect data on space occupancy profiles. Phase II of the project demonstrated that a network of several sensors provides a more accurate measure of occupancy than is possible using systems based on single monitoring points. This phase also established that analysis algorithms could be applied to the sensor network data stream to improve the accuracy of system performance in energy management and security applications. In Phase III of the project, the sensor network from Phase I was complemented by a control strategy developed based on the results from the first two project phases: this controller was implemented in a small sample of work areas, and applied to lighting control. Two additional technologies were developed in the course of completing the project. A prototype web-based display that portrays the current status of each detector in a sensor network monitoring building occupancy was designed and implemented. A new capability that enables occupancy sensors in a sensor network to dynamically set the 'time delay' interval based on ongoing occupant behavior in the space was also designed and implemented.« less

A Survey of Geosensor Networks: Advances in Dynamic Environmental Monitoring

PubMed Central

Nittel, Silvia

2009-01-01

In the recent decade, several technology trends have influenced the field of geosciences in significant ways. The first trend is the more readily available technology of ubiquitous wireless communication networks and progress in the development of low-power, short-range radio-based communication networks, the miniaturization of computing and storage platforms as well as the development of novel microsensors and sensor materials. All three trends have changed the type of dynamic environmental phenomena that can be detected, monitored and reacted to. Another important aspect is the real-time data delivery of novel platforms today. In this paper, I will survey the field of geosensor networks, and mainly focus on the technology of small-scale geosensor networks, example applications and their feasibility and lessons learnt as well as the current research questions posed by using this technology today. Furthermore, my objective is to investigate how this technology can be embedded in the current landscape of intelligent sensor platforms in the geosciences and identify its place and purpose. PMID:22346721

Analysis of power management and system latency in wireless sensor networks

NASA Astrophysics Data System (ADS)

Oswald, Matthew T.; Rohwer, Judd A.; Forman, Michael A.

2004-08-01

Successful power management in a wireless sensor network requires optimization of the protocols which affect energy-consumption on each node and the aggregate effects across the larger network. System optimization for a given deployment scenario requires an analysis and trade off of desired node and network features with their associated costs. The sleep protocol for an energy-efficient wireless sensor network for event detection, target classification, and target tracking developed at Sandia National Laboratories is presented. The dynamic source routing (DSR) algorithm is chosen to reduce network maintenance overhead, while providing a self-configuring and self-healing network architecture. A method for determining the optimal sleep time is developed and presented, providing reference data which spans several orders of magnitude. Message timing diagrams show, that a node in a five-node cluster, employing an optimal cyclic single-radio sleep protocol, consumes 3% more energy and incurs a 16-s increase latency than nodes employing the more complex dual-radio STEM protocol.

EMMNet: sensor networking for electricity meter monitoring.

PubMed

Lin, Zhi-Ting; Zheng, Jie; Ji, Yu-Sheng; Zhao, Bao-Hua; Qu, Yu-Gui; Huang, Xu-Dong; Jiang, Xiu-Fang

2010-01-01

Smart sensors are emerging as a promising technology for a large number of application domains. This paper presents a collection of requirements and guidelines that serve as a basis for a general smart sensor architecture to monitor electricity meters. It also presents an electricity meter monitoring network, named EMMNet, comprised of data collectors, data concentrators, hand-held devices, a centralized server, and clients. EMMNet provides long-distance communication capabilities, which make it suitable suitable for complex urban environments. In addition, the operational cost of EMMNet is low, compared with other existing remote meter monitoring systems based on GPRS. A new dynamic tree protocol based on the application requirements which can significantly improve the reliability of the network is also proposed. We are currently conducting tests on five networks and investigating network problems for further improvements. Evaluation results indicate that EMMNet enhances the efficiency and accuracy in the reading, recording, and calibration of electricity meters.

EMMNet: Sensor Networking for Electricity Meter Monitoring

PubMed Central

Lin, Zhi-Ting; Zheng, Jie; Ji, Yu-Sheng; Zhao, Bao-Hua; Qu, Yu-Gui; Huang, Xu-Dong; Jiang, Xiu-Fang

2010-01-01

Smart sensors are emerging as a promising technology for a large number of application domains. This paper presents a collection of requirements and guidelines that serve as a basis for a general smart sensor architecture to monitor electricity meters. It also presents an electricity meter monitoring network, named EMMNet, comprised of data collectors, data concentrators, hand-held devices, a centralized server, and clients. EMMNet provides long-distance communication capabilities, which make it suitable suitable for complex urban environments. In addition, the operational cost of EMMNet is low, compared with other existing remote meter monitoring systems based on GPRS. A new dynamic tree protocol based on the application requirements which can significantly improve the reliability of the network is also proposed. We are currently conducting tests on five networks and investigating network problems for further improvements. Evaluation results indicate that EMMNet enhances the efficiency and accuracy in the reading, recording, and calibration of electricity meters. PMID:22163551

Energy optimization in mobile sensor networks

NASA Astrophysics Data System (ADS)

Yu, Shengwei

Mobile sensor networks are considered to consist of a network of mobile robots, each of which has computation, communication and sensing capabilities. Energy efficiency is a critical issue in mobile sensor networks, especially when mobility (i.e., locomotion control), routing (i.e., communications) and sensing are unique characteristics of mobile robots for energy optimization. This thesis focuses on the problem of energy optimization of mobile robotic sensor networks, and the research results can be extended to energy optimization of a network of mobile robots that monitors the environment, or a team of mobile robots that transports materials from stations to stations in a manufacturing environment. On the energy optimization of mobile robotic sensor networks, our research focuses on the investigation and development of distributed optimization algorithms to exploit the mobility of robotic sensor nodes for network lifetime maximization. In particular, the thesis studies these five problems: 1. Network-lifetime maximization by controlling positions of networked mobile sensor robots based on local information with distributed optimization algorithms; 2. Lifetime maximization of mobile sensor networks with energy harvesting modules; 3. Lifetime maximization using joint design of mobility and routing; 4. Optimal control for network energy minimization; 5. Network lifetime maximization in mobile visual sensor networks. In addressing the first problem, we consider only the mobility strategies of the robotic relay nodes in a mobile sensor network in order to maximize its network lifetime. By using variable substitutions, the original problem is converted into a convex problem, and a variant of the sub-gradient method for saddle-point computation is developed for solving this problem. An optimal solution is obtained by the method. Computer simulations show that mobility of robotic sensors can significantly prolong the lifetime of the whole robotic sensor network while consuming negligible amount of energy for mobility cost. For the second problem, the problem is extended to accommodate mobile robotic nodes with energy harvesting capability, which makes it a non-convex optimization problem. The non-convexity issue is tackled by using the existing sequential convex approximation method, based on which we propose a novel procedure of modified sequential convex approximation that has fast convergence speed. For the third problem, the proposed procedure is used to solve another challenging non-convex problem, which results in utilizing mobility and routing simultaneously in mobile robotic sensor networks to prolong the network lifetime. The results indicate that joint design of mobility and routing has an edge over other methods in prolonging network lifetime, which is also the justification for the use of mobility in mobile sensor networks for energy efficiency purpose. For the fourth problem, we include the dynamics of the robotic nodes in the problem by modeling the networked robotic system using hybrid systems theory. A novel distributed method for the networked hybrid system is used to solve the optimal moving trajectories for robotic nodes and optimal network links, which are not answered by previous approaches. Finally, the fact that mobility is more effective in prolonging network lifetime for a data-intensive network leads us to apply our methods to study mobile visual sensor networks, which are useful in many applications. We investigate the joint design of mobility, data routing, and encoding power to help improving the video quality while maximizing the network lifetime. This study leads to a better understanding of the role mobility can play in data-intensive surveillance sensor networks.

Further Structural Intelligence for Sensors Cluster Technology in Manufacturing

PubMed Central

Mekid, Samir

2006-01-01

With the ever increasing complex sensing and actuating tasks in manufacturing plants, intelligent sensors cluster in hybrid networks becomes a rapidly expanding area. They play a dominant role in many fields from macro and micro scale. Global object control and the ability to self organize into fault-tolerant and scalable systems are expected for high level applications. In this paper, new structural concepts of intelligent sensors and networks with new intelligent agents are presented. Embedding new functionalities to dynamically manage cooperative agents for autonomous machines are interesting key enabling technologies most required in manufacturing for zero defects production.

Toward controlling perturbations in robotic sensor networks

NASA Astrophysics Data System (ADS)

Banerjee, Ashis G.; Majumder, Saikat R.

2014-06-01

Robotic sensor networks (RSNs), which consist of networks of sensors placed on mobile robots, are being increasingly used for environment monitoring applications. In particular, a lot of work has been done on simultaneous localization and mapping of the robots, and optimal sensor placement for environment state estimation1. The deployment of RSNs, however, remains challenging in harsh environments where the RSNs have to deal with significant perturbations in the forms of wind gusts, turbulent water flows, sand storms, or blizzards that disrupt inter-robot communication and individual robot stability. Hence, there is a need to be able to control such perturbations and bring the networks to desirable states with stable nodes (robots) and minimal operational performance (environment sensing). Recent work has demonstrated the feasibility of controlling the non-linear dynamics in other communication networks like emergency management systems and power grids by introducing compensatory perturbations to restore network stability and operation2. In this paper, we develop a computational framework to investigate the usefulness of this approach for RSNs in marine environments. Preliminary analysis shows promising performance and identifies bounds on the original perturbations within which it is possible to control the networks.

Cluster-based adaptive power control protocol using Hidden Markov Model for Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Vinutha, C. B.; Nalini, N.; Nagaraja, M.

2017-06-01

This paper presents strategies for an efficient and dynamic transmission power control technique, in order to reduce packet drop and hence energy consumption of power-hungry sensor nodes operated in highly non-linear channel conditions of Wireless Sensor Networks. Besides, we also focus to prolong network lifetime and scalability by designing cluster-based network structure. Specifically we consider weight-based clustering approach wherein, minimum significant node is chosen as Cluster Head (CH) which is computed stemmed from the factors distance, remaining residual battery power and received signal strength (RSS). Further, transmission power control schemes to fit into dynamic channel conditions are meticulously implemented using Hidden Markov Model (HMM) where probability transition matrix is formulated based on the observed RSS measurements. Typically, CH estimates initial transmission power of its cluster members (CMs) from RSS using HMM and broadcast this value to its CMs for initialising their power value. Further, if CH finds that there are variations in link quality and RSS of the CMs, it again re-computes and optimises the transmission power level of the nodes using HMM to avoid packet loss due noise interference. We have demonstrated our simulation results to prove that our technique efficiently controls the power levels of sensing nodes to save significant quantity of energy for different sized network.

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors.

PubMed

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-09-21

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.

Accurate Natural Trail Detection Using a Combination of a Deep Neural Network and Dynamic Programming.

PubMed

Adhikari, Shyam Prasad; Yang, Changju; Slot, Krzysztof; Kim, Hyongsuk

2018-01-10

This paper presents a vision sensor-based solution to the challenging problem of detecting and following trails in highly unstructured natural environments like forests, rural areas and mountains, using a combination of a deep neural network and dynamic programming. The deep neural network (DNN) concept has recently emerged as a very effective tool for processing vision sensor signals. A patch-based DNN is trained with supervised data to classify fixed-size image patches into "trail" and "non-trail" categories, and reshaped to a fully convolutional architecture to produce trail segmentation map for arbitrary-sized input images. As trail and non-trail patches do not exhibit clearly defined shapes or forms, the patch-based classifier is prone to misclassification, and produces sub-optimal trail segmentation maps. Dynamic programming is introduced to find an optimal trail on the sub-optimal DNN output map. Experimental results showing accurate trail detection for real-world trail datasets captured with a head mounted vision system are presented.

Path planning in GPS-denied environments via collective intelligence of distributed sensor networks

NASA Astrophysics Data System (ADS)

Jha, Devesh K.; Chattopadhyay, Pritthi; Sarkar, Soumik; Ray, Asok

2016-05-01

This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin's car-like robot.

Distributed fault detection over sensor networks with Markovian switching topologies

NASA Astrophysics Data System (ADS)

Ge, Xiaohua; Han, Qing-Long

2014-05-01

This paper deals with the distributed fault detection for discrete-time Markov jump linear systems over sensor networks with Markovian switching topologies. The sensors are scatteredly deployed in the sensor field and the fault detectors are physically distributed via a communication network. The system dynamics changes and sensing topology variations are modeled by a discrete-time Markov chain with incomplete mode transition probabilities. Each of these sensor nodes firstly collects measurement outputs from its all underlying neighboring nodes, processes these data in accordance with the Markovian switching topologies, and then transmits the processed data to the remote fault detector node. Network-induced delays and accumulated data packet dropouts are incorporated in the data transmission between the sensor nodes and the distributed fault detector nodes through the communication network. To generate localized residual signals, mode-independent distributed fault detection filters are proposed. By means of the stochastic Lyapunov functional approach, the residual system performance analysis is carried out such that the overall residual system is stochastically stable and the error between each residual signal and the fault signal is made as small as possible. Furthermore, a sufficient condition on the existence of the mode-independent distributed fault detection filters is derived in the simultaneous presence of incomplete mode transition probabilities, Markovian switching topologies, network-induced delays, and accumulated data packed dropouts. Finally, a stirred-tank reactor system is given to show the effectiveness of the developed theoretical results.

Key Management Scheme Based on Route Planning of Mobile Sink in Wireless Sensor Networks.

PubMed

Zhang, Ying; Liang, Jixing; Zheng, Bingxin; Jiang, Shengming; Chen, Wei

2016-01-29

In many wireless sensor network application scenarios the key management scheme with a Mobile Sink (MS) should be fully investigated. This paper proposes a key management scheme based on dynamic clustering and optimal-routing choice of MS. The concept of Traveling Salesman Problem with Neighbor areas (TSPN) in dynamic clustering for data exchange is proposed, and the selection probability is used in MS route planning. The proposed scheme extends static key management to dynamic key management by considering the dynamic clustering and mobility of MSs, which can effectively balance the total energy consumption during the activities. Considering the different resources available to the member nodes and sink node, the session key between cluster head and MS is established by modified an ECC encryption with Diffie-Hellman key exchange (ECDH) algorithm and the session key between member node and cluster head is built with a binary symmetric polynomial. By analyzing the security of data storage, data transfer and the mechanism of dynamic key management, the proposed scheme has more advantages to help improve the resilience of the key management system of the network on the premise of satisfying higher connectivity and storage efficiency.

Distributed Water Pollution Source Localization with Mobile UV-Visible Spectrometer Probes in Wireless Sensor Networks.

PubMed

Ma, Junjie; Meng, Fansheng; Zhou, Yuexi; Wang, Yeyao; Shi, Ping

2018-02-16

Pollution accidents that occur in surface waters, especially in drinking water source areas, greatly threaten the urban water supply system. During water pollution source localization, there are complicated pollutant spreading conditions and pollutant concentrations vary in a wide range. This paper provides a scalable total solution, investigating a distributed localization method in wireless sensor networks equipped with mobile ultraviolet-visible (UV-visible) spectrometer probes. A wireless sensor network is defined for water quality monitoring, where unmanned surface vehicles and buoys serve as mobile and stationary nodes, respectively. Both types of nodes carry UV-visible spectrometer probes to acquire in-situ multiple water quality parameter measurements, in which a self-adaptive optical path mechanism is designed to flexibly adjust the measurement range. A novel distributed algorithm, called Dual-PSO, is proposed to search for the water pollution source, where one particle swarm optimization (PSO) procedure computes the water quality multi-parameter measurements on each node, utilizing UV-visible absorption spectra, and another one finds the global solution of the pollution source position, regarding mobile nodes as particles. Besides, this algorithm uses entropy to dynamically recognize the most sensitive parameter during searching. Experimental results demonstrate that online multi-parameter monitoring of a drinking water source area with a wide dynamic range is achieved by this wireless sensor network and water pollution sources are localized efficiently with low-cost mobile node paths.

Distributed Water Pollution Source Localization with Mobile UV-Visible Spectrometer Probes in Wireless Sensor Networks

PubMed Central

Zhou, Yuexi; Wang, Yeyao; Shi, Ping

2018-01-01

Pollution accidents that occur in surface waters, especially in drinking water source areas, greatly threaten the urban water supply system. During water pollution source localization, there are complicated pollutant spreading conditions and pollutant concentrations vary in a wide range. This paper provides a scalable total solution, investigating a distributed localization method in wireless sensor networks equipped with mobile ultraviolet-visible (UV-visible) spectrometer probes. A wireless sensor network is defined for water quality monitoring, where unmanned surface vehicles and buoys serve as mobile and stationary nodes, respectively. Both types of nodes carry UV-visible spectrometer probes to acquire in-situ multiple water quality parameter measurements, in which a self-adaptive optical path mechanism is designed to flexibly adjust the measurement range. A novel distributed algorithm, called Dual-PSO, is proposed to search for the water pollution source, where one particle swarm optimization (PSO) procedure computes the water quality multi-parameter measurements on each node, utilizing UV-visible absorption spectra, and another one finds the global solution of the pollution source position, regarding mobile nodes as particles. Besides, this algorithm uses entropy to dynamically recognize the most sensitive parameter during searching. Experimental results demonstrate that online multi-parameter monitoring of a drinking water source area with a wide dynamic range is achieved by this wireless sensor network and water pollution sources are localized efficiently with low-cost mobile node paths. PMID:29462929

Fuzzy Logic Control Based QoS Management in Wireless Sensor/Actuator Networks

PubMed Central

Xia, Feng; Zhao, Wenhong; Sun, Youxian; Tian, Yu-Chu

2007-01-01

Wireless sensor/actuator networks (WSANs) are emerging rapidly as a new generation of sensor networks. Despite intensive research in wireless sensor networks (WSNs), limited work has been found in the open literature in the field of WSANs. In particular, quality-of-service (QoS) management in WSANs remains an important issue yet to be investigated. As an attempt in this direction, this paper develops a fuzzy logic control based QoS management (FLC-QM) scheme for WSANs with constrained resources and in dynamic and unpredictable environments. Taking advantage of the feedback control technology, this scheme deals with the impact of unpredictable changes in traffic load on the QoS of WSANs. It utilizes a fuzzy logic controller inside each source sensor node to adapt sampling period to the deadline miss ratio associated with data transmission from the sensor to the actuator. The deadline miss ratio is maintained at a pre-determined desired level so that the required QoS can be achieved. The FLC-QM has the advantages of generality, scalability, and simplicity. Simulation results show that the FLC-QM can provide WSANs with QoS support. PMID:28903288

Bridge condition assessment and load rating using dynamic response.

DOT National Transportation Integrated Search

2014-07-01

This report describes a method for the overall condition assessment and load rating of prestressed box beam : (PSBB) bridges based on their dynamic response collected through wireless sensor networks (WSNs). Due to a : large inventory of deficient an...

Development of a Three Dimensional Wireless Sensor Network for Terrain-Climate Research in Remote Mountainous Environments

NASA Astrophysics Data System (ADS)

Kavanagh, K.; Davis, A.; Gessler, P.; Hess, H.; Holden, Z.; Link, T. E.; Newingham, B. A.; Smith, A. M.; Robinson, P.

2011-12-01

Developing sensor networks that are robust enough to perform in the world's remote regions is critical since these regions serve as important benchmarks compared to human-dominated areas. Paradoxically, the factors that make these remote, natural sites challenging for sensor networking are often what make them indispensable for climate change research. We aim to overcome these challenges by developing a three-dimensional sensor network arrayed across a topoclimatic gradient (1100-1800 meters) in a wilderness area in central Idaho. Development of this sensor array builds upon advances in sensing, networking, and power supply technologies coupled with experiences of the multidisciplinary investigators in conducting research in remote mountainous locations. The proposed gradient monitoring network will provide near real-time data from a three-dimensional (3-D) array of sensors measuring biophysical parameters used in ecosystem process models. The network will monitor atmospheric carbon dioxide concentration, humidity, air and soil temperature, soil water content, precipitation, incoming and outgoing shortwave and longwave radiation, snow depth, wind speed and direction, tree stem growth and leaf wetness at time intervals ranging from seconds to days. The long-term goal of this project is to realize a transformative integration of smart sensor networks adaptively communicating data in real-time to ultimately achieve a 3-D visualization of ecosystem processes within remote mountainous regions. Process models will be the interface between the visualization platforms and the sensor network. This will allow us to better predict how non-human dominated terrestrial and aquatic ecosystems function and respond to climate dynamics. Access to the data will be ensured as part of the Northwest Knowledge Network being developed at the University of Idaho, through ongoing Idaho NSF-funded cyber infrastructure initiatives, and existing data management systems funded by NSF, such as the CUAHSI Hydrologic Information System (HIS). These efforts will enhance cross-disciplinary understanding of natural and anthropogenic influences on ecosystem function and ultimately inform decision-making.

Optimal stimulus scheduling for active estimation of evoked brain networks.

PubMed

Kafashan, MohammadMehdi; Ching, ShiNung

2015-12-01

We consider the problem of optimal probing to learn connections in an evoked dynamic network. Such a network, in which each edge measures an input-output relationship between sites in sensor/actuator-space, is relevant to emerging applications in neural mapping and neural connectivity estimation. We show that the problem of scheduling nodes to a probe (i.e., stimulate) amounts to a problem of optimal sensor scheduling. By formulating the evoked network in state-space, we show that the solution to the greedy probing strategy has a convenient form and, under certain conditions, is optimal over a finite horizon. We adopt an expectation maximization technique to update the state-space parameters in an online fashion and demonstrate the efficacy of the overall approach in a series of detailed numerical examples. The proposed method provides a principled means to actively probe time-varying connections in neuronal networks. The overall method can be implemented in real time and is particularly well-suited to applications in stimulation-based cortical mapping in which the underlying network dynamics are changing over time.

Optimal stimulus scheduling for active estimation of evoked brain networks

NASA Astrophysics Data System (ADS)

Kafashan, MohammadMehdi; Ching, ShiNung

2015-12-01

Objective. We consider the problem of optimal probing to learn connections in an evoked dynamic network. Such a network, in which each edge measures an input-output relationship between sites in sensor/actuator-space, is relevant to emerging applications in neural mapping and neural connectivity estimation. Approach. We show that the problem of scheduling nodes to a probe (i.e., stimulate) amounts to a problem of optimal sensor scheduling. Main results. By formulating the evoked network in state-space, we show that the solution to the greedy probing strategy has a convenient form and, under certain conditions, is optimal over a finite horizon. We adopt an expectation maximization technique to update the state-space parameters in an online fashion and demonstrate the efficacy of the overall approach in a series of detailed numerical examples. Significance. The proposed method provides a principled means to actively probe time-varying connections in neuronal networks. The overall method can be implemented in real time and is particularly well-suited to applications in stimulation-based cortical mapping in which the underlying network dynamics are changing over time.

A Robust and Energy-Efficient Transport Protocol for Cognitive Radio Sensor Networks

PubMed Central

Salim, Shelly; Moh, Sangman

2014-01-01

A cognitive radio sensor network (CRSN) is a wireless sensor network in which sensor nodes are equipped with cognitive radio. CRSNs benefit from cognitive radio capabilities such as dynamic spectrum access and transmission parameters reconfigurability; but cognitive radio also brings additional challenges and leads to higher energy consumption. Motivated to improve the energy efficiency in CRSNs, we propose a robust and energy-efficient transport protocol (RETP). The novelties of RETP are two-fold: (I) it combines distributed channel sensing and channel decision with centralized schedule-based data transmission; and (II) it differentiates the types of data transmission on the basis of data content and adopts different acknowledgment methods for different transmission types. To the best of our knowledge, no transport layer protocols have yet been designed for CRSNs. Simulation results show that the proposed protocol achieves remarkably longer network lifetime and shorter event-detection delay compared to those achieved with a conventional transport protocol, while simultaneously preserving event-detection reliability. PMID:25333288

Energy-efficient pulse-coupled synchronization strategy design for wireless sensor networks through reduced idle listening

PubMed Central

Wang, Yongqiang; Núñez, Felipe; Doyle, Francis J.

2013-01-01

Synchronization is crucial to wireless sensor networks due to their decentralized structure. We propose an energy-efficient pulse-coupled synchronization strategy to achieve this goal. The basic idea is to reduce idle listening by intentionally introducing a large refractory period in the sensors’ cooperation. The large refractory period greatly reduces idle listening in each oscillation period, and is analytically proven to have no influence on the time to synchronization. Hence, it significantly reduces the total energy consumption in a synchronization process. A topology control approach tailored for pulse-coupled synchronization is given to guarantee a k-edge strongly connected interaction topology, which is tolerant to communication-link failures. The topology control approach is totally decentralized and needs no information exchange among sensors, and it is applicable to dynamic network topologies as well. This facilitates a completely decentralized implementation of the synchronization strategy. The strategy is applicable to mobile sensor networks, too. QualNet case studies confirm the effectiveness of the synchronization strategy. PMID:24307831

Risk assessment by dynamic representation of vulnerability, exploitation, and impact

NASA Astrophysics Data System (ADS)

Cam, Hasan

2015-05-01

Assessing and quantifying cyber risk accurately in real-time is essential to providing security and mission assurance in any system and network. This paper presents a modeling and dynamic analysis approach to assessing cyber risk of a network in real-time by representing dynamically its vulnerabilities, exploitations, and impact using integrated Bayesian network and Markov models. Given the set of vulnerabilities detected by a vulnerability scanner in a network, this paper addresses how its risk can be assessed by estimating in real-time the exploit likelihood and impact of vulnerability exploitation on the network, based on real-time observations and measurements over the network. The dynamic representation of the network in terms of its vulnerabilities, sensor measurements, and observations is constructed dynamically using the integrated Bayesian network and Markov models. The transition rates of outgoing and incoming links of states in hidden Markov models are used in determining exploit likelihood and impact of attacks, whereas emission rates help quantify the attack states of vulnerabilities. Simulation results show the quantification and evolving risk scores over time for individual and aggregated vulnerabilities of a network.

Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management.

PubMed

Cruz-Piris, Luis; Rivera, Diego; Fernandez, Susel; Marsa-Maestre, Ivan

2018-02-02

One of the biggest challenges in modern societies is to solve vehicular traffic problems. Sensor networks in traffic environments have contributed to improving the decision-making process of Intelligent Transportation Systems. However, one of the limiting factors for the effectiveness of these systems is in the deployment of sensors to provide accurate information about the traffic. Our proposal is using the centrality measurement of a graph as a base to locate the best locations for sensor installation in a traffic network. After integrating these sensors in a simulation scenario, we define a Multi-Agent Systems composed of three types of agents: traffic light management agents, traffic jam detection agents, and agents that control the traffic lights at an intersection. The ultimate goal of these Multi-Agent Systems is to improve the trip duration for vehicles in the network. To validate our solution, we have developed the needed elements for modelling the sensors and agents in the simulation environment. We have carried out experiments using the Simulation of Urban MObility (SUMO) traffic simulator and the Travel and Activity PAtterns Simulation (TAPAS) Cologne traffic scenario. The obtained results show that our proposal allows to reduce the sensor network while still obtaining relevant information to have a global view of the environment. Finally, regarding the Multi-Agent Systems, we have carried out experiments that show that our proposal is able to improve other existing solutions such as conventional traffic light management systems (static or dynamic) in terms of reduction of vehicle trip duration and reduction of the message exchange overhead in the sensor network.

Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management

PubMed Central

2018-01-01

One of the biggest challenges in modern societies is to solve vehicular traffic problems. Sensor networks in traffic environments have contributed to improving the decision-making process of Intelligent Transportation Systems. However, one of the limiting factors for the effectiveness of these systems is in the deployment of sensors to provide accurate information about the traffic. Our proposal is using the centrality measurement of a graph as a base to locate the best locations for sensor installation in a traffic network. After integrating these sensors in a simulation scenario, we define a Multi-Agent Systems composed of three types of agents: traffic light management agents, traffic jam detection agents, and agents that control the traffic lights at an intersection. The ultimate goal of these Multi-Agent Systems is to improve the trip duration for vehicles in the network. To validate our solution, we have developed the needed elements for modelling the sensors and agents in the simulation environment. We have carried out experiments using the Simulation of Urban MObility (SUMO) traffic simulator and the Travel and Activity PAtterns Simulation (TAPAS) Cologne traffic scenario. The obtained results show that our proposal allows to reduce the sensor network while still obtaining relevant information to have a global view of the environment. Finally, regarding the Multi-Agent Systems, we have carried out experiments that show that our proposal is able to improve other existing solutions such as conventional traffic light management systems (static or dynamic) in terms of reduction of vehicle trip duration and reduction of the message exchange overhead in the sensor network. PMID:29393884

A radar-enabled collaborative sensor network integrating COTS technology for surveillance and tracking.

PubMed

Kozma, Robert; Wang, Lan; Iftekharuddin, Khan; McCracken, Ernest; Khan, Muhammad; Islam, Khandakar; Bhurtel, Sushil R; Demirer, R Murat

2012-01-01

The feasibility of using Commercial Off-The-Shelf (COTS) sensor nodes is studied in a distributed network, aiming at dynamic surveillance and tracking of ground targets. Data acquisition by low-cost (<$50 US) miniature low-power radar through a wireless mote is described. We demonstrate the detection, ranging and velocity estimation, classification and tracking capabilities of the mini-radar, and compare results to simulations and manual measurements. Furthermore, we supplement the radar output with other sensor modalities, such as acoustic and vibration sensors. This method provides innovative solutions for detecting, identifying, and tracking vehicles and dismounts over a wide area in noisy conditions. This study presents a step towards distributed intelligent decision support and demonstrates effectiveness of small cheap sensors, which can complement advanced technologies in certain real-life scenarios.

International Space Station Future Correlation Analysis Improvements

NASA Technical Reports Server (NTRS)

Laible, Michael R.; Pinnamaneni, Murthy; Sugavanam, Sujatha; Grygier, Michael

2018-01-01

Ongoing modal analyses and model correlation are performed on different configurations of the International Space Station (ISS). These analyses utilize on-orbit dynamic measurements collected using four main ISS instrumentation systems: External Wireless Instrumentation System (EWIS), Internal Wireless Instrumentation System (IWIS), Space Acceleration Measurement System (SAMS), and Structural Dynamic Measurement System (SDMS). Remote Sensor Units (RSUs) are network relay stations that acquire flight data from sensors. Measured data is stored in the Remote Sensor Unit (RSU) until it receives a command to download data via RF to the Network Control Unit (NCU). Since each RSU has its own clock, it is necessary to synchronize measurements before analysis. Imprecise synchronization impacts analysis results. A study was performed to evaluate three different synchronization techniques: (i) measurements visually aligned to analytical time-response data using model comparison, (ii) Frequency Domain Decomposition (FDD), and (iii) lag from cross-correlation to align measurements. This paper presents the results of this study.

Ground Monitoring Neotropical Dry Forests: A Sensor Network for Forest and Microclimate Dynamics in Semi-Arid Environments (Enviro-Net°)

NASA Astrophysics Data System (ADS)

Rankine, C. J.; Sánchez-Azofeifa, G.

2011-12-01

In the face of unprecedented global change driven by anthropogenic pressure on natural systems it has become imperative to monitor and better understand potential shifts in ecosystem functioning and services from local to global scales. The utilization of automated sensors technologies offers numerous advantages over traditional on-site ecosystem surveying techniques and, as a result, sensor networks are becoming a powerful tool in environmental monitoring programs. Tropical forests, renowned for their biodiversity, are important regulators of land-atmosphere fluxes yet the seasonally dry tropical forests, which account for 40% of forested ecosystems in the American tropics, have been severely degraded over the past several decades and not much is known of their capacity to recover. With less than 1% of these forests protected, our ability to monitor the dynamics and quantify changes in the remaining primary and recovering secondary tropical dry forests is vital to understanding mechanisms of ecosystem stress responses and climate feedback with respect to annual productivity and desertification processes in the tropics. The remote sensing component of the Tropi-Dry: Human and Biophysical Dimensions of Tropical Dry Forests in the Americas research network supports a network of long-term tropical ecosystem monitoring platforms which focus on the dynamics of seasonally dry tropical forests in the Americas. With over 25 sensor station deployments operating across a latitudinal gradient in Mexico, Costa Rica, Brazil, and Argentina continuously collecting hyper-temporal sensory input based on standardized deployment parameters, this monitoring system is unique among tropical environments. Technologies used in the network include optical canopy phenology towers, understory wireless sensing networks, above and below ground microclimate stations, and digital cameras. Sensory data streams are uploaded to a cyber-infrastructure initiative, denominated Enviro-Net°, for data storage, management, visualization, and retrieval for further analysis. The use of tower and ground-based optical sensor networks and meteorological monitoring instrumentation has proven effective in capturing seasonal growth patterns in primary and secondary forest stands. Furthermore, the observed trends in above and below ground microclimate variables are shown to closely correlate with in-situ vegetative indices (NDVI and EVI) across study sites. These long-term environmental sensory data streams provide valuable insights as to how these threatened semi-arid ecosystems regenerate after disturbances and how they respond to environmental stress such as climate change in the tropical and sub-tropical latitudes.

Propagation, cascades, and agreement dynamics in complex communication and social networks

NASA Astrophysics Data System (ADS)

Lu, Qiming

Many modern and important technological, social, information and infrastructure systems can be viewed as complex systems with a large number of interacting components. Models of complex networks and dynamical interactions, as well as their applications are of fundamental interests in many aspects. Here, several stylized models of multiplex propagation and opinion dynamics are investigated on complex and empirical social networks. We first investigate cascade dynamics in threshold-controlled (multiplex) propagation on random geometric networks. We find that such local dynamics can serve as an efficient, robust, and reliable prototypical activation protocol in sensor networks in responding to various alarm scenarios. We also consider the same dynamics on a modified network by adding a few long-range communication links, resulting in a small-world network. We find that such construction can further enhance and optimize the speed of the network's response, while keeping energy consumption at a manageable level. We also investigate a prototypical agent-based model, the Naming Game, on two-dimensional random geometric networks. The Naming Game [A. Baronchelli et al., J. Stat. Mech.: Theory Exp. (2006) P06014.] is a minimal model, employing local communications that captures the emergence of shared communication schemes (languages) in a population of autonomous semiotic agents. Implementing the Naming Games with local broadcasts on random geometric graphs, serves as a model for agreement dynamics in large-scale, autonomously operating wireless sensor networks. Further, it captures essential features of the scaling properties of the agreement process for spatially-embedded autonomous agents. Among the relevant observables capturing the temporal properties of the agreement process, we investigate the cluster-size distribution and the distribution of the agreement times, both exhibiting dynamic scaling. We also present results for the case when a small density of long-range communication links are added on top of the random geometric graph, resulting in a "small-world"-like network and yielding a significantly reduced time to reach global agreement. We construct a finite-size scaling analysis for the agreement times in this case. When applying the model of Naming Game on empirical social networks, this stylized agent-based model captures essential features of agreement dynamics in a network of autonomous agents, corresponding to the development of shared classification schemes in a network of artificial agents or opinion spreading and social dynamics in social networks. Our study focuses on the impact that communities in the underlying social graphs have on the outcome of the agreement process. We find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the Naming Game in these networks maintains clusters of coexisting opinions indefinitely. Further, we investigate agent-based network strategies to facilitate convergence to global consensus.

A Space Weather Forecasting System with Multiple Satellites Based on a Self-Recognizing Network

PubMed Central

Tokumitsu, Masahiro; Ishida, Yoshiteru

2014-01-01

This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron flux (>2 MeV). The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic field and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron flux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing. PMID:24803190

A space weather forecasting system with multiple satellites based on a self-recognizing network.

PubMed

Tokumitsu, Masahiro; Ishida, Yoshiteru

2014-05-05

This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron flux (>2 MeV). The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic field and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron flux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing.

Dynamic Data Driven Applications Systems (DDDAS)

DTIC Science & Technology

2013-03-06

INS •  Chip-scale atomic clocks •  Ad hoc networks •  Polymorphic networks •  Agile networks •  Laser communications •  Frequency-agile RF...atomi clocks •  Ad hoc networks •  Polymorphic networks •  Agile networks •  Laser co munications •  Frequency-agile RF systems...Real-Time Doppler Wind Wind field Sensor observations Energy Estimation Atmospheric Models for On-line Planning Planning and Control

A Q-Learning-Based Delay-Aware Routing Algorithm to Extend the Lifetime of Underwater Sensor Networks.

PubMed

Jin, Zhigang; Ma, Yingying; Su, Yishan; Li, Shuo; Fu, Xiaomei

2017-07-19

Underwater sensor networks (UWSNs) have become a hot research topic because of their various aquatic applications. As the underwater sensor nodes are powered by built-in batteries which are difficult to replace, extending the network lifetime is a most urgent need. Due to the low and variable transmission speed of sound, the design of reliable routing algorithms for UWSNs is challenging. In this paper, we propose a Q-learning based delay-aware routing (QDAR) algorithm to extend the lifetime of underwater sensor networks. In QDAR, a data collection phase is designed to adapt to the dynamic environment. With the application of the Q-learning technique, QDAR can determine a global optimal next hop rather than a greedy one. We define an action-utility function in which residual energy and propagation delay are both considered for adequate routing decisions. Thus, the QDAR algorithm can extend the network lifetime by uniformly distributing the residual energy and provide lower end-to-end delay. The simulation results show that our protocol can yield nearly the same network lifetime, and can reduce the end-to-end delay by 20-25% compared with a classic lifetime-extended routing protocol (QELAR).

Virtual Wireless Sensor Networks: Adaptive Brain-Inspired Configuration for Internet of Things Applications

PubMed Central

Toyonaga, Shinya; Kominami, Daichi; Murata, Masayuki

2016-01-01

Many researchers are devoting attention to the so-called “Internet of Things” (IoT), and wireless sensor networks (WSNs) are regarded as a critical technology for realizing the communication infrastructure of the future, including the IoT. Against this background, virtualization is a crucial technique for the integration of multiple WSNs. Designing virtualized WSNs for actual environments will require further detailed studies. Within the IoT environment, physical networks can undergo dynamic change, and so, many problems exist that could prevent applications from running without interruption when using the existing approaches. In this paper, we show an overall architecture that is suitable for constructing and running virtual wireless sensor network (VWSN) services within a VWSN topology. Our approach provides users with a reliable VWSN network by assigning redundant resources according to each user’s demand and providing a recovery method to incorporate environmental changes. We tested this approach by simulation experiment, with the results showing that the VWSN network is reliable in many cases, although physical deployment of sensor nodes and the modular structure of the VWSN will be quite important to the stability of services within the VWSN topology. PMID:27548177

A Q-Learning-Based Delay-Aware Routing Algorithm to Extend the Lifetime of Underwater Sensor Networks

PubMed Central

Ma, Yingying; Su, Yishan; Li, Shuo; Fu, Xiaomei

2017-01-01

Underwater sensor networks (UWSNs) have become a hot research topic because of their various aquatic applications. As the underwater sensor nodes are powered by built-in batteries which are difficult to replace, extending the network lifetime is a most urgent need. Due to the low and variable transmission speed of sound, the design of reliable routing algorithms for UWSNs is challenging. In this paper, we propose a Q-learning based delay-aware routing (QDAR) algorithm to extend the lifetime of underwater sensor networks. In QDAR, a data collection phase is designed to adapt to the dynamic environment. With the application of the Q-learning technique, QDAR can determine a global optimal next hop rather than a greedy one. We define an action-utility function in which residual energy and propagation delay are both considered for adequate routing decisions. Thus, the QDAR algorithm can extend the network lifetime by uniformly distributing the residual energy and provide lower end-to-end delay. The simulation results show that our protocol can yield nearly the same network lifetime, and can reduce the end-to-end delay by 20–25% compared with a classic lifetime-extended routing protocol (QELAR). PMID:28753951

Virtual Wireless Sensor Networks: Adaptive Brain-Inspired Configuration for Internet of Things Applications.

PubMed

Toyonaga, Shinya; Kominami, Daichi; Murata, Masayuki

2016-08-19

Many researchers are devoting attention to the so-called "Internet of Things" (IoT), and wireless sensor networks (WSNs) are regarded as a critical technology for realizing the communication infrastructure of the future, including the IoT. Against this background, virtualization is a crucial technique for the integration of multiple WSNs. Designing virtualized WSNs for actual environments will require further detailed studies. Within the IoT environment, physical networks can undergo dynamic change, and so, many problems exist that could prevent applications from running without interruption when using the existing approaches. In this paper, we show an overall architecture that is suitable for constructing and running virtual wireless sensor network (VWSN) services within a VWSN topology. Our approach provides users with a reliable VWSN network by assigning redundant resources according to each user's demand and providing a recovery method to incorporate environmental changes. We tested this approach by simulation experiment, with the results showing that the VWSN network is reliable in many cases, although physical deployment of sensor nodes and the modular structure of the VWSN will be quite important to the stability of services within the VWSN topology.

Minimum expected delay-based routing protocol (MEDR) for Delay Tolerant Mobile Sensor Networks.

PubMed

Feng, Yong; Liu, Ming; Wang, Xiaomin; Gong, Haigang

2010-01-01

It is a challenging work to develop efficient routing protocols for Delay Tolerant Mobile Sensor Networks (DTMSNs), which have several unique characteristics such as sensor mobility, intermittent connectivity, energy limit, and delay tolerability. In this paper, we propose a new routing protocol called Minimum Expected Delay-based Routing (MEDR) tailored for DTMSNs. MEDR achieves a good routing performance by finding and using the connected paths formed dynamically by mobile sensors. In MEDR, each sensor maintains two important parameters: Minimum Expected Delay (MED) and its expiration time. According to MED, messages will be delivered to the sensor that has at least a connected path with their hosting nodes, and has the shortest expected delay to communication directly with the sink node. Because of the changing network topology, the path is fragile and volatile, so we use the expiration time of MED to indicate the valid time of the path, and avoid wrong transmissions. Simulation results show that the proposed MEDR achieves a higher message delivery ratio with lower transmission overhead and data delivery delay than other DTMSN routing approaches.

Bio-inspired secure data mules for medical sensor network

NASA Astrophysics Data System (ADS)

Muraleedharan, Rajani; Gao, Weihua; Osadciw, Lisa A.

2010-04-01

Medical sensor network consist of heterogeneous nodes, wireless, mobile and wired with varied functionality. The resources at each sensor require to be exploited minimally while sensitive information is sensed and communicated to its access points using secure data mules. In this paper, we analyze the flat architecture, where different functionality and priority information require varied resources forms a non-deterministic polynomial-time hard problem. Hence, a bio-inspired data mule that helps to obtain dynamic multi-objective solution with minimal resource and secure path is applied. The performance of the proposed approach is based on reduced latency, data delivery rate and resource cost.

General visual robot controller networks via artificial evolution

NASA Astrophysics Data System (ADS)

Cliff, David; Harvey, Inman; Husbands, Philip

1993-08-01

We discuss recent results from our ongoing research concerning the application of artificial evolution techniques (i.e., an extended form of genetic algorithm) to the problem of developing `neural' network controllers for visually guided robots. The robot is a small autonomous vehicle with extremely low-resolution vision, employing visual sensors which could readily be constructed from discrete analog components. In addition to visual sensing, the robot is equipped with a small number of mechanical tactile sensors. Activity from the sensors is fed to a recurrent dynamical artificial `neural' network, which acts as the robot controller, providing signals to motors governing the robot's motion. Prior to presentation of new results, this paper summarizes our rationale and past work, which has demonstrated that visually guided control networks can arise without any explicit specification that visual processing should be employed: the evolutionary process opportunistically makes use of visual information if it is available.

MASM: a market architecture for sensor management in distributed sensor networks

NASA Astrophysics Data System (ADS)

Viswanath, Avasarala; Mullen, Tracy; Hall, David; Garga, Amulya

2005-03-01

Rapid developments in sensor technology and its applications have energized research efforts towards devising a firm theoretical foundation for sensor management. Ubiquitous sensing, wide bandwidth communications and distributed processing provide both opportunities and challenges for sensor and process control and optimization. Traditional optimization techniques do not have the ability to simultaneously consider the wildly non-commensurate measures involved in sensor management in a single optimization routine. Market-oriented programming provides a valuable and principled paradigm to designing systems to solve this dynamic and distributed resource allocation problem. We have modeled the sensor management scenario as a competitive market, wherein the sensor manager holds a combinatorial auction to sell the various items produced by the sensors and the communication channels. However, standard auction mechanisms have been found not to be directly applicable to the sensor management domain. For this purpose, we have developed a specialized market architecture MASM (Market architecture for Sensor Management). In MASM, the mission manager is responsible for deciding task allocations to the consumers and their corresponding budgets and the sensor manager is responsible for resource allocation to the various consumers. In addition to having a modified combinatorial winner determination algorithm, MASM has specialized sensor network modules that address commensurability issues between consumers and producers in the sensor network domain. A preliminary multi-sensor, multi-target simulation environment has been implemented to test the performance of the proposed system. MASM outperformed the information theoretic sensor manager in meeting the mission objectives in the simulation experiments.

Ultrasensitive Wearable Soft Strain Sensors of Conductive, Self-healing, and Elastic Hydrogels with Synergistic "Soft and Hard" Hybrid Networks.

PubMed

Liu, Yan-Jun; Cao, Wen-Tao; Ma, Ming-Guo; Wan, Pengbo

2017-08-02

Robust, stretchable, and strain-sensitive hydrogels have recently attracted immense research interest because of their potential application in wearable strain sensors. The integration of the synergistic characteristics of decent mechanical properties, reliable self-healing capability, and high sensing sensitivity for fabricating conductive, elastic, self-healing, and strain-sensitive hydrogels is still a great challenge. Inspired by the mechanically excellent and self-healing biological soft tissues with hierarchical network structures, herein, functional network hydrogels are fabricated by the interconnection between a "soft" homogeneous polymer network and a "hard" dynamic ferric (Fe 3+ ) cross-linked cellulose nanocrystals (CNCs-Fe 3+ ) network. Under stress, the dynamic CNCs-Fe 3+ coordination bonds act as sacrificial bonds to efficiently dissipate energy, while the homogeneous polymer network leads to a smooth stress-transfer, which enables the hydrogels to achieve unusual mechanical properties, such as excellent mechanical strength, robust toughness, and stretchability, as well as good self-recovery property. The hydrogels demonstrate autonomously self-healing capability in only 5 min without the need of any stimuli or healing agents, ascribing to the reorganization of CNCs and Fe 3+ via ionic coordination. Furthermore, the resulted hydrogels display tunable electromechanical behavior with sensitive, stable, and repeatable variations in resistance upon mechanical deformations. Based on the tunable electromechanical behavior, the hydrogels can act as a wearable strain sensor to monitor finger joint motions, breathing, and even the slight blood pulse. This strategy of building synergistic "soft and hard" structures is successful to integrate the decent mechanical properties, reliable self-healing capability, and high sensing sensitivity together for assembling a high-performance, flexible, and wearable strain sensor.

Measurement correction method for force sensor used in dynamic pressure calibration based on artificial neural network optimized by genetic algorithm

NASA Astrophysics Data System (ADS)

Gu, Tingwei; Kong, Deren; Shang, Fei; Chen, Jing

2017-12-01

We present an optimization algorithm to obtain low-uncertainty dynamic pressure measurements from a force-transducer-based device. In this paper, the advantages and disadvantages of the methods that are commonly used to measure the propellant powder gas pressure, the applicable scope of dynamic pressure calibration devices, and the shortcomings of the traditional comparison calibration method based on the drop-weight device are firstly analysed in detail. Then, a dynamic calibration method for measuring pressure using a force sensor based on a drop-weight device is introduced. This method can effectively save time when many pressure sensors are calibrated simultaneously and extend the life of expensive reference sensors. However, the force sensor is installed between the drop-weight and the hammerhead by transition pieces through the connection mode of bolt fastening, which causes adverse effects such as additional pretightening and inertia forces. To solve these effects, the influence mechanisms of the pretightening force, the inertia force and other influence factors on the force measurement are theoretically analysed. Then a measurement correction method for the force measurement is proposed based on an artificial neural network optimized by a genetic algorithm. The training and testing data sets are obtained from calibration tests, and the selection criteria for the key parameters of the correction model is discussed. The evaluation results for the test data show that the correction model can effectively improve the force measurement accuracy of the force sensor. Compared with the traditional high-accuracy comparison calibration method, the percentage difference of the impact-force-based measurement is less than 0.6% and the relative uncertainty of the corrected force value is 1.95%, which can meet the requirements of engineering applications.

TDM interrogation of intensity-modulated USFBGs network based on multichannel lasers.

PubMed

Rohollahnejad, Jalal; Xia, Li; Cheng, Rui; Ran, Yanli; Rahubadde, Udaya; Zhou, Jiaao; Zhu, Lin

2017-01-23

We report a large-scale multi-channel fiber sensing network, where ultra-short FBGs (USFBGs) instead of conventional narrow-band ultra-weak FBGs are used as the sensors. In the time division multiplexing scheme of the network, each grating response is resolved as three adjacent discrete peaks. The central wavelengths of USFBGs are tracked with the differential detection, which is achieved by calculating the peak-to-peak ratio of two maximum peaks. Compared with previous large-scale hybrid multiplexing sensing networks (e.g., WDM/TDM) which typically have relatively low interrogation speed and very high complexity, the proposed system can achieve interrogation of all channel sensors through very fast and simple intensity measurements with a broad dynamic range. A proof-of-concept experiment with twenty USFBGs, at two wavelength channels, was performed and a fast static strain measurements were demonstrated, with a high average sensitivity of ~0.54dB/µƐ and wide dynamic range of over ~3000µƐ. The channel to channel switching time was 10ms and total network interrogation time was 50ms.

Staggered scheduling of sensor estimation and fusion for tracking over long-haul links

DOE PAGES

Liu, Qiang; Rao, Nageswara S. V.; Wang, Xin

2016-08-01

Networked sensing can be found in a multitude of real-world applications. Here, we focus on the communication-and computation-constrained long-haul sensor networks, where sensors are remotely deployed over a vast geographical area to perform certain tasks. Of special interest is a class of such networks where sensors take measurements of one or more dynamic targets and send their state estimates to a remote fusion center via long-haul satellite links. The severe loss and delay over such links can easily reduce the amount of sensor data received by the fusion center, thereby limiting the potential information fusion gain and resulting in suboptimalmore » tracking performance. In this paper, starting with the temporal-domain staggered estimation for an individual sensor, we explore the impact of the so-called intra-state prediction and retrodiction on estimation errors. We then investigate the effect of such estimation scheduling across different sensors on the spatial-domain fusion performance, where the sensing time epochs across sensors are scheduled in an asynchronous and staggered manner. In particular, the impact of communication delay and loss as well as sensor bias on such scheduling is explored by means of numerical and simulation studies that demonstrate the validity of our analysis.« less

Cross Deployment Networking and Systematic Performance Analysis of Underwater Wireless Sensor Networks.

PubMed

Wei, Zhengxian; Song, Min; Yin, Guisheng; Wang, Hongbin; Ma, Xuefei; Song, Houbing

2017-07-12

Underwater wireless sensor networks (UWSNs) have become a new hot research area. However, due to the work dynamics and harsh ocean environment, how to obtain an UWSN with the best systematic performance while deploying as few sensor nodes as possible and setting up self-adaptive networking is an urgent problem that needs to be solved. Consequently, sensor deployment, networking, and performance calculation of UWSNs are challenging issues, hence the study in this paper centers on this topic and three relevant methods and models are put forward. Firstly, the normal body-centered cubic lattice to cross body-centered cubic lattice (CBCL) has been improved, and a deployment process and topology generation method are built. Then most importantly, a cross deployment networking method (CDNM) for UWSNs suitable for the underwater environment is proposed. Furthermore, a systematic quar-performance calculation model (SQPCM) is proposed from an integrated perspective, in which the systematic performance of a UWSN includes coverage, connectivity, durability and rapid-reactivity. Besides, measurement models are established based on the relationship between systematic performance and influencing parameters. Finally, the influencing parameters are divided into three types, namely, constraint parameters, device performance and networking parameters. Based on these, a networking parameters adjustment method (NPAM) for optimized systematic performance of UWSNs has been presented. The simulation results demonstrate that the approach proposed in this paper is feasible and efficient in networking and performance calculation of UWSNs.

Cross Deployment Networking and Systematic Performance Analysis of Underwater Wireless Sensor Networks

PubMed Central

Wei, Zhengxian; Song, Min; Yin, Guisheng; Wang, Hongbin; Ma, Xuefei

2017-01-01

Underwater wireless sensor networks (UWSNs) have become a new hot research area. However, due to the work dynamics and harsh ocean environment, how to obtain an UWSN with the best systematic performance while deploying as few sensor nodes as possible and setting up self-adaptive networking is an urgent problem that needs to be solved. Consequently, sensor deployment, networking, and performance calculation of UWSNs are challenging issues, hence the study in this paper centers on this topic and three relevant methods and models are put forward. Firstly, the normal body-centered cubic lattice to cross body-centered cubic lattice (CBCL) has been improved, and a deployment process and topology generation method are built. Then most importantly, a cross deployment networking method (CDNM) for UWSNs suitable for the underwater environment is proposed. Furthermore, a systematic quar-performance calculation model (SQPCM) is proposed from an integrated perspective, in which the systematic performance of a UWSN includes coverage, connectivity, durability and rapid-reactivity. Besides, measurement models are established based on the relationship between systematic performance and influencing parameters. Finally, the influencing parameters are divided into three types, namely, constraint parameters, device performance and networking parameters. Based on these, a networking parameters adjustment method (NPAM) for optimized systematic performance of UWSNs has been presented. The simulation results demonstrate that the approach proposed in this paper is feasible and efficient in networking and performance calculation of UWSNs. PMID:28704959

A Radar-Enabled Collaborative Sensor Network Integrating COTS Technology for Surveillance and Tracking

PubMed Central

Kozma, Robert; Wang, Lan; Iftekharuddin, Khan; McCracken, Ernest; Khan, Muhammad; Islam, Khandakar; Bhurtel, Sushil R.; Demirer, R. Murat

2012-01-01

The feasibility of using Commercial Off-The-Shelf (COTS) sensor nodes is studied in a distributed network, aiming at dynamic surveillance and tracking of ground targets. Data acquisition by low-cost (<$50 US) miniature low-power radar through a wireless mote is described. We demonstrate the detection, ranging and velocity estimation, classification and tracking capabilities of the mini-radar, and compare results to simulations and manual measurements. Furthermore, we supplement the radar output with other sensor modalities, such as acoustic and vibration sensors. This method provides innovative solutions for detecting, identifying, and tracking vehicles and dismounts over a wide area in noisy conditions. This study presents a step towards distributed intelligent decision support and demonstrates effectiveness of small cheap sensors, which can complement advanced technologies in certain real-life scenarios. PMID:22438713

An energy-efficient and secure hybrid algorithm for wireless sensor networks using a mobile data collector

NASA Astrophysics Data System (ADS)

Dayananda, Karanam Ravichandran; Straub, Jeremy

2017-05-01

This paper proposes a new hybrid algorithm for security, which incorporates both distributed and hierarchal approaches. It uses a mobile data collector (MDC) to collect information in order to save energy of sensor nodes in a wireless sensor network (WSN) as, in most networks, these sensor nodes have limited energy. Wireless sensor networks are prone to security problems because, among other things, it is possible to use a rogue sensor node to eavesdrop on or alter the information being transmitted. To prevent this, this paper introduces a security algorithm for MDC-based WSNs. A key use of this algorithm is to protect the confidentiality of the information sent by the sensor nodes. The sensor nodes are deployed in a random fashion and form group structures called clusters. Each cluster has a cluster head. The cluster head collects data from the other nodes using the time-division multiple access protocol. The sensor nodes send their data to the cluster head for transmission to the base station node for further processing. The MDC acts as an intermediate node between the cluster head and base station. The MDC, using its dynamic acyclic graph path, collects the data from the cluster head and sends it to base station. This approach is useful for applications including warfighting, intelligent building and medicine. To assess the proposed system, the paper presents a comparison of its performance with other approaches and algorithms that can be used for similar purposes.

Evolving RBF neural networks for adaptive soft-sensor design.

PubMed

Alexandridis, Alex

2013-12-01

This work presents an adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in order to evolve an RBF network, which approximates the unknown system based on input-output data from it. The methodology gradually builds the RBF network model, based on two separate levels of adaptation: On the first level, the structure of the hidden layer is modified by adding or deleting RBF centers, while on the second level, the synaptic weights are adjusted with the recursive least squares with exponential forgetting algorithm. The proposed approach is tested on two different systems, namely a simulated nonlinear DC Motor and a real industrial reactor. The results show that the produced soft-sensors can be successfully applied to model the two nonlinear systems. A comparison with two different adaptive modeling techniques, namely a dynamic evolving neural-fuzzy inference system (DENFIS) and neural networks trained with online backpropagation, highlights the advantages of the proposed methodology.

Understanding dynamic pattern and process across spatial scales in river systems using simultaneous deployments of in situ sensors

NASA Astrophysics Data System (ADS)

Wollheim, W. M.; Mulukutla, G.; Cook, C.; Carey, R. O.

2014-12-01

Biogeochemical conditions throughout aquatic landscapes are spatially varied and temporally dynamic due to interactions of upstream land use, climate, hydrologic responses, and internal aquatic processes. One of the key goals in aquatic ecosystem ecology is to parse the upstream influences of terrestrial and aquatic processes on local conditions, which becomes progressively more difficult as watershed size increases and as processes are altered by diverse human activities. Simultaneous deployments of high frequency, in situ aquatic sensors for multiple constituents (e.g. NO3-N, CDOM, turbidity, conductivity, D.O., water temperature, along with flow) offer a new approach for understanding patterns along the aquatic continuum. For this talk, we explore strategies for deployments within single watersheds to improve understanding of terrestrial and aquatic processes. We address applications regarding mobilization of non-point nutrient sources across temporal scales, interactions with land use and watershed size, and the importance of aquatic processes. We also explore ways in which simultaneous sensor deployments can be designed to improve parameterization and testing of river network biogeochemical models. We will provide several specific examples using conductivity, nitrate and carbon from ongoing sensor deployments in New England, USA. We expect that improved deployments of sensors and sensor networks will benefit the management of critical freshwater resources.

Self-organizing sensing and actuation for automatic control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cheng, George Shu-Xing

A Self-Organizing Process Control Architecture is introduced with a Sensing Layer, Control Layer, Actuation Layer, Process Layer, as well as Self-Organizing Sensors (SOS) and Self-Organizing Actuators (SOA). A Self-Organizing Sensor for a process variable with one or multiple input variables is disclosed. An artificial neural network (ANN) based dynamic modeling mechanism as part of the Self-Organizing Sensor is described. As a case example, a Self-Organizing Soft-Sensor for CFB Boiler Bed Height is presented. Also provided is a method to develop a Self-Organizing Sensor.

Track classification within wireless sensor network

NASA Astrophysics Data System (ADS)

Doumerc, Robin; Pannetier, Benjamin; Moras, Julien; Dezert, Jean; Canevet, Loic

2017-05-01

In this paper, we present our study on track classification by taking into account environmental information and target estimated states. The tracker uses several motion model adapted to different target dynamics (pedestrian, ground vehicle and SUAV, i.e. small unmanned aerial vehicle) and works in centralized architecture. The main idea is to explore both: classification given by heterogeneous sensors and classification obtained with our fusion module. The fusion module, presented in his paper, provides a class on each track according to track location, velocity and associated uncertainty. To model the likelihood on each class, a fuzzy approach is used considering constraints on target capability to move in the environment. Then the evidential reasoning approach based on Dempster-Shafer Theory (DST) is used to perform a time integration of this classifier output. The fusion rules are tested and compared on real data obtained with our wireless sensor network.In order to handle realistic ground target tracking scenarios, we use an autonomous smart computer deposited in the surveillance area. After the calibration step of the heterogeneous sensor network, our system is able to handle real data from a wireless ground sensor network. The performance of this system is evaluated in a real exercise for intelligence operation ("hunter hunt" scenario).

Detecting malicious chaotic signals in wireless sensor network

NASA Astrophysics Data System (ADS)

Upadhyay, Ranjit Kumar; Kumari, Sangeeta

2018-02-01

In this paper, an e-epidemic Susceptible-Infected-Vaccinated (SIV) model has been proposed to analyze the effect of node immunization and worms attacking dynamics in wireless sensor network. A modified nonlinear incidence rate with cyrtoid type functional response has been considered using sleep and active mode approach. Detailed stability analysis and the sufficient criteria for the persistence of the model system have been established. We also established different types of bifurcation analysis for different equilibria at different critical points of the control parameters. We performed a detailed Hopf bifurcation analysis and determine the direction and stability of the bifurcating periodic solutions using center manifold theorem. Numerical simulations are carried out to confirm the theoretical results. The impact of the control parameters on the dynamics of the model system has been investigated and malicious chaotic signals are detected. Finally, we have analyzed the effect of time delay on the dynamics of the model system.

Impact of Sensor Misplacement on Dynamic Time Warping Based Human Activity Recognition using Wearable Computers.

PubMed

Kale, Nimish; Lee, Jaeseong; Lotfian, Reza; Jafari, Roozbeh

2012-10-01

Daily living activity monitoring is important for early detection of the onset of many diseases and for improving quality of life especially in elderly. A wireless wearable network of inertial sensor nodes can be used to observe daily motions. Continuous stream of data generated by these sensor networks can be used to recognize the movements of interest. Dynamic Time Warping (DTW) is a widely used signal processing method for time-series pattern matching because of its robustness to variations in time and speed as opposed to other template matching methods. Despite this flexibility, for the application of activity recognition, DTW can only find the similarity between the template of a movement and the incoming samples, when the location and orientation of the sensor remains unchanged. Due to this restriction, small sensor misplacements can lead to a decrease in the classification accuracy. In this work, we adopt DTW distance as a feature for real-time detection of human daily activities like sit to stand in the presence of sensor misplacement. To measure this performance of DTW, we need to create a large number of sensor configurations while the sensors are rotated or misplaced. Creating a large number of closely spaced sensors is impractical. To address this problem, we use the marker based optical motion capture system and generate simulated inertial sensor data for different locations and orientations on the body. We study the performance of the DTW under these conditions to determine the worst-case sensor location variations that the algorithm can accommodate.

Source Authentication for Code Dissemination Supporting Dynamic Packet Size in Wireless Sensor Networks.

PubMed

Kim, Daehee; Kim, Dongwan; An, Sunshin

2016-07-09

Code dissemination in wireless sensor networks (WSNs) is a procedure for distributing a new code image over the air in order to update programs. Due to the fact that WSNs are mostly deployed in unattended and hostile environments, secure code dissemination ensuring authenticity and integrity is essential. Recent works on dynamic packet size control in WSNs allow enhancing the energy efficiency of code dissemination by dynamically changing the packet size on the basis of link quality. However, the authentication tokens attached by the base station become useless in the next hop where the packet size can vary according to the link quality of the next hop. In this paper, we propose three source authentication schemes for code dissemination supporting dynamic packet size. Compared to traditional source authentication schemes such as μTESLA and digital signatures, our schemes provide secure source authentication under the environment, where the packet size changes in each hop, with smaller energy consumption.

Source Authentication for Code Dissemination Supporting Dynamic Packet Size in Wireless Sensor Networks †

PubMed Central

Kim, Daehee; Kim, Dongwan; An, Sunshin

2016-01-01

Code dissemination in wireless sensor networks (WSNs) is a procedure for distributing a new code image over the air in order to update programs. Due to the fact that WSNs are mostly deployed in unattended and hostile environments, secure code dissemination ensuring authenticity and integrity is essential. Recent works on dynamic packet size control in WSNs allow enhancing the energy efficiency of code dissemination by dynamically changing the packet size on the basis of link quality. However, the authentication tokens attached by the base station become useless in the next hop where the packet size can vary according to the link quality of the next hop. In this paper, we propose three source authentication schemes for code dissemination supporting dynamic packet size. Compared to traditional source authentication schemes such as μTESLA and digital signatures, our schemes provide secure source authentication under the environment, where the packet size changes in each hop, with smaller energy consumption. PMID:27409616

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors

PubMed Central

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-01-01

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors. PMID:28934163

Dynamic Reconfiguration of Security Policies in Wireless Sensor Networks

PubMed Central

Pinto, Mónica; Gámez, Nadia; Fuentes, Lidia; Amor, Mercedes; Horcas, José Miguel; Ayala, Inmaculada

2015-01-01

Providing security and privacy to wireless sensor nodes (WSNs) is very challenging, due to the heterogeneity of sensor nodes and their limited capabilities in terms of energy, processing power and memory. The applications for these systems run in a myriad of sensors with different low-level programming abstractions, limited capabilities and different routing protocols. This means that applications for WSNs need mechanisms for self-adaptation and for self-protection based on the dynamic adaptation of the algorithms used to provide security. Dynamic software product lines (DSPLs) allow managing both variability and dynamic software adaptation, so they can be considered a key technology in successfully developing self-protected WSN applications. In this paper, we propose a self-protection solution for WSNs based on the combination of the INTER-TRUST security framework (a solution for the dynamic negotiation and deployment of security policies) and the FamiWare middleware (a DSPL approach to automatically configure and reconfigure instances of a middleware for WSNs). We evaluate our approach using a case study from the intelligent transportation system domain. PMID:25746093

Spatio-temporal interpolation of soil moisture in 3D+T using automated sensor network data

NASA Astrophysics Data System (ADS)

Gasch, C.; Hengl, T.; Magney, T. S.; Brown, D. J.; Gräler, B.

2014-12-01

Soil sensor networks provide frequent in situ measurements of dynamic soil properties at fixed locations, producing data in 2- or 3-dimensions and through time (2D+T and 3D+T). Spatio-temporal interpolation of 3D+T point data produces continuous estimates that can then be used for prediction at unsampled times and locations, as input for process models, and can simply aid in visualization of properties through space and time. Regression-kriging with 3D and 2D+T data has successfully been implemented, but currently the field of geostatistics lacks an analytical framework for modeling 3D+T data. Our objective is to develop robust 3D+T models for mapping dynamic soil data that has been collected with high spatial and temporal resolution. For this analysis, we use data collected from a sensor network installed on the R.J. Cook Agronomy Farm (CAF), a 37-ha Long-Term Agro-Ecosystem Research (LTAR) site in Pullman, WA. For five years, the sensors have collected hourly measurements of soil volumetric water content at 42 locations and five depths. The CAF dataset also includes a digital elevation model and derivatives, a soil unit description map, crop rotations, electromagnetic induction surveys, daily meteorological data, and seasonal satellite imagery. The soil-water sensor data, combined with the spatial and temporal covariates, provide an ideal dataset for developing 3D+T models. The presentation will include preliminary results and address main implementation strategies.

Optimum Sensors Integration for Multi-Sensor Multi-Target Environment for Ballistic Missile Defense Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Imam, Neena; Barhen, Jacob; Glover, Charles Wayne

2012-01-01

Multi-sensor networks may face resource limitations in a dynamically evolving multiple target tracking scenario. It is necessary to task the sensors efficiently so that the overall system performance is maximized within the system constraints. The central sensor resource manager may control the sensors to meet objective functions that are formulated to meet system goals such as minimization of track loss, maximization of probability of target detection, and minimization of track error. This paper discusses the variety of techniques that may be utilized to optimize sensor performance for either near term gain or future reward over a longer time horizon.

Network Coded Cooperative Communication in a Real-Time Wireless Hospital Sensor Network.

PubMed

Prakash, R; Balaji Ganesh, A; Sivabalan, Somu

2017-05-01

The paper presents a network coded cooperative communication (NC-CC) enabled wireless hospital sensor network architecture for monitoring health as well as postural activities of a patient. A wearable device, referred as a smartband is interfaced with pulse rate, body temperature sensors and an accelerometer along with wireless protocol services, such as Bluetooth and Radio-Frequency transceiver and Wi-Fi. The energy efficiency of wearable device is improved by embedding a linear acceleration based transmission duty cycling algorithm (NC-DRDC). The real-time demonstration is carried-out in a hospital environment to evaluate the performance characteristics, such as power spectral density, energy consumption, signal to noise ratio, packet delivery ratio and transmission offset. The resource sharing and energy efficiency features of network coding technique are improved by proposing an algorithm referred as network coding based dynamic retransmit/rebroadcast decision control (LA-TDC). From the experimental results, it is observed that the proposed LA-TDC algorithm reduces network traffic and end-to-end delay by an average of 27.8% and 21.6%, respectively than traditional network coded wireless transmission. The wireless architecture is deployed in a hospital environment and results are then successfully validated.

Dynamic Control of Adsorption Sensitivity for Photo-EMF-Based Ammonia Gas Sensors Using a Wireless Network

PubMed Central

Vashpanov, Yuriy; Choo, Hyunseung; Kim, Dongsoo Stephen

2011-01-01

This paper proposes an adsorption sensitivity control method that uses a wireless network and illumination light intensity in a photo-electromagnetic field (EMF)-based gas sensor for measurements in real time of a wide range of ammonia concentrations. The minimum measurement error for a range of ammonia concentration from 3 to 800 ppm occurs when the gas concentration magnitude corresponds with the optimal intensity of the illumination light. A simulation with LabView-engineered modules for automatic control of a new intelligent computer system was conducted to improve measurement precision over a wide range of gas concentrations. This gas sensor computer system with wireless network technology could be useful in the chemical industry for automatic detection and measurement of hazardous ammonia gas levels in real time. PMID:22346680

Monitoring and Discovery for Self-Organized Network Management in Virtualized and Software Defined Networks

PubMed Central

Valdivieso Caraguay, Ángel Leonardo; García Villalba, Luis Javier

2017-01-01

This paper presents the Monitoring and Discovery Framework of the Self-Organized Network Management in Virtualized and Software Defined Networks SELFNET project. This design takes into account the scalability and flexibility requirements needed by 5G infrastructures. In this context, the present framework focuses on gathering and storing the information (low-level metrics) related to physical and virtual devices, cloud environments, flow metrics, SDN traffic and sensors. Similarly, it provides the monitoring data as a generic information source in order to allow the correlation and aggregation tasks. Our design enables the collection and storing of information provided by all the underlying SELFNET sublayers, including the dynamically onboarded and instantiated SDN/NFV Apps, also known as SELFNET sensors. PMID:28362346

Monitoring and Discovery for Self-Organized Network Management in Virtualized and Software Defined Networks.

PubMed

Caraguay, Ángel Leonardo Valdivieso; Villalba, Luis Javier García

2017-03-31

This paper presents the Monitoring and Discovery Framework of the Self-Organized Network Management in Virtualized and Software Defined Networks SELFNET project. This design takes into account the scalability and flexibility requirements needed by 5G infrastructures. In this context, the present framework focuses on gathering and storing the information (low-level metrics) related to physical and virtual devices, cloud environments, flow metrics, SDN traffic and sensors. Similarly, it provides the monitoring data as a generic information source in order to allow the correlation and aggregation tasks. Our design enables the collection and storing of information provided by all the underlying SELFNET sublayers, including the dynamically onboarded and instantiated SDN/NFV Apps, also known as SELFNET sensors.

A Movement-Assisted Deployment of Collaborating Autonomous Sensors for Indoor and Outdoor Environment Monitoring

PubMed Central

Niewiadomska-Szynkiewicz, Ewa; Sikora, Andrzej; Marks, Michał

2016-01-01

Using mobile robots or unmanned vehicles to assist optimal wireless sensors deployment in a working space can significantly enhance the capability to investigate unknown environments. This paper addresses the issues of the application of numerical optimization and computer simulation techniques to on-line calculation of a wireless sensor network topology for monitoring and tracking purposes. We focus on the design of a self-organizing and collaborative mobile network that enables a continuous data transmission to the data sink (base station) and automatically adapts its behavior to changes in the environment to achieve a common goal. The pre-defined and self-configuring approaches to the mobile-based deployment of sensors are compared and discussed. A family of novel algorithms for the optimal placement of mobile wireless devices for permanent monitoring of indoor and outdoor dynamic environments is described. They employ a network connectivity-maintaining mobility model utilizing the concept of the virtual potential function for calculating the motion trajectories of platforms carrying sensors. Their quality and utility have been justified through simulation experiments and are discussed in the final part of the paper. PMID:27649186

A Comparison of Alternative Distributed Dynamic Cluster Formation Techniques for Industrial Wireless Sensor Networks.

PubMed

Gholami, Mohammad; Brennan, Robert W

2016-01-06

In this paper, we investigate alternative distributed clustering techniques for wireless sensor node tracking in an industrial environment. The research builds on extant work on wireless sensor node clustering by reporting on: (1) the development of a novel distributed management approach for tracking mobile nodes in an industrial wireless sensor network; and (2) an objective comparison of alternative cluster management approaches for wireless sensor networks. To perform this comparison, we focus on two main clustering approaches proposed in the literature: pre-defined clusters and ad hoc clusters. These approaches are compared in the context of their reconfigurability: more specifically, we investigate the trade-off between the cost and the effectiveness of competing strategies aimed at adapting to changes in the sensing environment. To support this work, we introduce three new metrics: a cost/efficiency measure, a performance measure, and a resource consumption measure. The results of our experiments show that ad hoc clusters adapt more readily to changes in the sensing environment, but this higher level of adaptability is at the cost of overall efficiency.

A Comparison of Alternative Distributed Dynamic Cluster Formation Techniques for Industrial Wireless Sensor Networks

PubMed Central

Gholami, Mohammad; Brennan, Robert W.

2016-01-01

In this paper, we investigate alternative distributed clustering techniques for wireless sensor node tracking in an industrial environment. The research builds on extant work on wireless sensor node clustering by reporting on: (1) the development of a novel distributed management approach for tracking mobile nodes in an industrial wireless sensor network; and (2) an objective comparison of alternative cluster management approaches for wireless sensor networks. To perform this comparison, we focus on two main clustering approaches proposed in the literature: pre-defined clusters and ad hoc clusters. These approaches are compared in the context of their reconfigurability: more specifically, we investigate the trade-off between the cost and the effectiveness of competing strategies aimed at adapting to changes in the sensing environment. To support this work, we introduce three new metrics: a cost/efficiency measure, a performance measure, and a resource consumption measure. The results of our experiments show that ad hoc clusters adapt more readily to changes in the sensing environment, but this higher level of adaptability is at the cost of overall efficiency. PMID:26751447

A Movement-Assisted Deployment of Collaborating Autonomous Sensors for Indoor and Outdoor Environment Monitoring.

PubMed

Niewiadomska-Szynkiewicz, Ewa; Sikora, Andrzej; Marks, Michał

2016-09-14

Using mobile robots or unmanned vehicles to assist optimal wireless sensors deployment in a working space can significantly enhance the capability to investigate unknown environments. This paper addresses the issues of the application of numerical optimization and computer simulation techniques to on-line calculation of a wireless sensor network topology for monitoring and tracking purposes. We focus on the design of a self-organizing and collaborative mobile network that enables a continuous data transmission to the data sink (base station) and automatically adapts its behavior to changes in the environment to achieve a common goal. The pre-defined and self-configuring approaches to the mobile-based deployment of sensors are compared and discussed. A family of novel algorithms for the optimal placement of mobile wireless devices for permanent monitoring of indoor and outdoor dynamic environments is described. They employ a network connectivity-maintaining mobility model utilizing the concept of the virtual potential function for calculating the motion trajectories of platforms carrying sensors. Their quality and utility have been justified through simulation experiments and are discussed in the final part of the paper.

Dynamic sensing model for accurate delectability of environmental phenomena using event wireless sensor network

NASA Astrophysics Data System (ADS)

Missif, Lial Raja; Kadhum, Mohammad M.

2017-09-01

Wireless Sensor Network (WSN) has been widely used for monitoring where sensors are deployed to operate independently to sense abnormal phenomena. Most of the proposed environmental monitoring systems are designed based on a predetermined sensing range which does not reflect the sensor reliability, event characteristics, and the environment conditions. Measuring of the capability of a sensor node to accurately detect an event within a sensing field is of great important for monitoring applications. This paper presents an efficient mechanism for even detection based on probabilistic sensing model. Different models have been presented theoretically in this paper to examine their adaptability and applicability to the real environment applications. The numerical results of the experimental evaluation have showed that the probabilistic sensing model provides accurate observation and delectability of an event, and it can be utilized for different environment scenarios.

Optimizing Retransmission Threshold in Wireless Sensor Networks

PubMed Central

Bi, Ran; Li, Yingshu; Tan, Guozhen; Sun, Liang

2016-01-01

The retransmission threshold in wireless sensor networks is critical to the latency of data delivery in the networks. However, existing works on data transmission in sensor networks did not consider the optimization of the retransmission threshold, and they simply set the same retransmission threshold for all sensor nodes in advance. The method did not take link quality and delay requirement into account, which decreases the probability of a packet passing its delivery path within a given deadline. This paper investigates the problem of finding optimal retransmission thresholds for relay nodes along a delivery path in a sensor network. The object of optimizing retransmission thresholds is to maximize the summation of the probability of the packet being successfully delivered to the next relay node or destination node in time. A dynamic programming-based distributed algorithm for finding optimal retransmission thresholds for relay nodes along a delivery path in the sensor network is proposed. The time complexity is OnΔ·max1≤i≤n{ui}, where ui is the given upper bound of the retransmission threshold of sensor node i in a given delivery path, n is the length of the delivery path and Δ is the given upper bound of the transmission delay of the delivery path. If Δ is greater than the polynomial, to reduce the time complexity, a linear programming-based (1+pmin)-approximation algorithm is proposed. Furthermore, when the ranges of the upper and lower bounds of retransmission thresholds are big enough, a Lagrange multiplier-based distributed O(1)-approximation algorithm with time complexity O(1) is proposed. Experimental results show that the proposed algorithms have better performance. PMID:27171092

A hybrid system identification methodology for wireless structural health monitoring systems based on dynamic substructuring

NASA Astrophysics Data System (ADS)

Dragos, Kosmas; Smarsly, Kay

2016-04-01

System identification has been employed in numerous structural health monitoring (SHM) applications. Traditional system identification methods usually rely on centralized processing of structural response data to extract information on structural parameters. However, in wireless SHM systems the centralized processing of structural response data introduces a significant communication bottleneck. Exploiting the merits of decentralization and on-board processing power of wireless SHM systems, many system identification methods have been successfully implemented in wireless sensor networks. While several system identification approaches for wireless SHM systems have been proposed, little attention has been paid to obtaining information on the physical parameters (e.g. stiffness, damping) of the monitored structure. This paper presents a hybrid system identification methodology suitable for wireless sensor networks based on the principles of component mode synthesis (dynamic substructuring). A numerical model of the monitored structure is embedded into the wireless sensor nodes in a distributed manner, i.e. the entire model is segmented into sub-models, each embedded into one sensor node corresponding to the substructure the sensor node is assigned to. The parameters of each sub-model are estimated by extracting local mode shapes and by applying the equations of the Craig-Bampton method on dynamic substructuring. The proposed methodology is validated in a laboratory test conducted on a four-story frame structure to demonstrate the ability of the methodology to yield accurate estimates of stiffness parameters. Finally, the test results are discussed and an outlook on future research directions is provided.

Statistical analysis of modal properties of a cable-stayed bridge through long-term structural health monitoring with wireless smart sensor networks

NASA Astrophysics Data System (ADS)

Asadollahi, Parisa; Li, Jian

2016-04-01

Understanding the dynamic behavior of complex structures such as long-span bridges requires dense deployment of sensors. Traditional wired sensor systems are generally expensive and time-consuming to install due to cabling. With wireless communication and on-board computation capabilities, wireless smart sensor networks have the advantages of being low cost, easy to deploy and maintain and therefore facilitate dense instrumentation for structural health monitoring. A long-term monitoring project was recently carried out for a cable-stayed bridge in South Korea with a dense array of 113 smart sensors, which feature the world's largest wireless smart sensor network for civil structural monitoring. This paper presents a comprehensive statistical analysis of the modal properties including natural frequencies, damping ratios and mode shapes of the monitored cable-stayed bridge. Data analyzed in this paper is composed of structural vibration signals monitored during a 12-month period under ambient excitations. The correlation between environmental temperature and the modal frequencies is also investigated. The results showed the long-term statistical structural behavior of the bridge, which serves as the basis for Bayesian statistical updating for the numerical model.

Real-time identification of indoor pollutant source positions based on neural network locator of contaminant sources and optimized sensor networks.

PubMed

Vukovic, Vladimir; Tabares-Velasco, Paulo Cesar; Srebric, Jelena

2010-09-01

A growing interest in security and occupant exposure to contaminants revealed a need for fast and reliable identification of contaminant sources during incidental situations. To determine potential contaminant source positions in outdoor environments, current state-of-the-art modeling methods use computational fluid dynamic simulations on parallel processors. In indoor environments, current tools match accidental contaminant distributions with cases from precomputed databases of possible concentration distributions. These methods require intensive computations in pre- and postprocessing. On the other hand, neural networks emerged as a tool for rapid concentration forecasting of outdoor environmental contaminants such as nitrogen oxides or sulfur dioxide. All of these modeling methods depend on the type of sensors used for real-time measurements of contaminant concentrations. A review of the existing sensor technologies revealed that no perfect sensor exists, but intensity of work in this area provides promising results in the near future. The main goal of the presented research study was to extend neural network modeling from the outdoor to the indoor identification of source positions, making this technology applicable to building indoor environments. The developed neural network Locator of Contaminant Sources was also used to optimize number and allocation of contaminant concentration sensors for real-time prediction of indoor contaminant source positions. Such prediction should take place within seconds after receiving real-time contaminant concentration sensor data. For the purpose of neural network training, a multizone program provided distributions of contaminant concentrations for known source positions throughout a test building. Trained networks had an output indicating contaminant source positions based on measured concentrations in different building zones. A validation case based on a real building layout and experimental data demonstrated the ability of this method to identify contaminant source positions. Future research intentions are focused on integration with real sensor networks and model improvements for much more complicated contamination scenarios.

Providing Self-Healing Ability for Wireless Sensor Node by Using Reconfigurable Hardware

PubMed Central

Yuan, Shenfang; Qiu, Lei; Gao, Shang; Tong, Yao; Yang, Weiwei

2012-01-01

Wireless sensor networks (WSNs) have received tremendous attention over the past ten years. In engineering applications of WSNs, a number of sensor nodes are usually spread across some specific geographical area. Some of these nodes have to work in harsh environments. Dependability of the Wireless Sensor Network (WSN) is very important for its successful applications in the engineering area. In ordinary research, when a node has a failure, it is usually discarded and the network is reorganized to ensure the normal operation of the WSN. Using appropriate WSN re-organization methods, though the sensor networks can be reorganized, this causes additional maintenance costs and sometimes still decreases the function of the networks. In those situations where the sensor networks cannot be reorganized, the performance of the whole WSN will surely be degraded. In order to ensure the reliable and low cost operation of WSNs, a method to develop a wireless sensor node with self-healing ability based on reconfigurable hardware is proposed in this paper. Two self-healing WSN node realization paradigms based on reconfigurable hardware are presented, including a redundancy-based self-healing paradigm and a whole FPAA/FPGA based self-healing paradigm. The nodes designed with the self-healing ability can dynamically change their node configurations to repair the nodes' hardware failures. To demonstrate these two paradigms, a strain sensor node is adopted as an illustration to show the concepts. Two strain WSN sensor nodes with self-healing ability are developed respectively according to the proposed self-healing paradigms. Evaluation experiments on self-healing ability and power consumption are performed. Experimental results show that the developed nodes can self-diagnose the failures and recover to a normal state automatically. The research presented can improve the robustness of WSNs and reduce the maintenance cost of WSNs in engineering applications. PMID:23202176

Predicting and Controlling Complex Networks

DTIC Science & Technology

2015-06-22

vulnerability and to generate a global view of network security against attacks. By deploying network sensors at particular points in the Internet ...48006, 1-6 (2011). 2 13. L. Huang and Y.-C. Lai, “Cascading dynamics in complex quantum networks,” Chaos 21, 025107, 1-6 (2011). This work was selected...by July 2011 issue of Virtual Journal of Quantum Information (http://www.vjquantuminfo.org). 14. W.-X. Wang, Y.-C. Lai, and D. Armbruster, “Cascading

System identification of a tied arch bridge using reference-based wireless sensor networks

NASA Astrophysics Data System (ADS)

Hietbrink, Colby; Whelan, Matthew J.

2012-04-01

Vibration-based methods of structural health monitoring are generally founded on the principle that localized damage to a structure would exhibit changes within the global dynamic response. Upon this basis, accelerometers provide a unique health monitoring strategy in that a distributed network of sensors provides the technical feasibility to isolate the onset of damage without requiring that any sensor be located exactly on or in close proximity to the damage. While in theory this may be sufficient, practical experience has shown significant improvement in the application of damage diagnostic routines when mode shapes characterized by strongly localized behavior of specific elements are captured by the instrumentation array. In traditional applications, this presents a challenge since the cost and complexity of cable-based systems often effectively limits the number of instrumented locations thereby constraining the modal parameter extraction to only global modal responses. The advent of the low-cost RF chip transceiver with wireless networking capabilities has afforded a means by which a substantial number of output locations can be measured through referencebased testing using large-scale wireless sensor networks. In the current study, this approach was applied to the Prairie du Chien Bridge over the Mississippi River to extract operational mode shapes with high spatial reconstruction, including strongly localized modes. The tied arch bridge was instrumented at over 230 locations with single-axis accelerometers conditioned and acquired over a high-rate lossless wireless sensor network with simultaneous sampling capabilities. Acquisition of the dynamic response of the web plates of the arch rib was specifically targeted within the instrumentation array for diagnostic purposes. Reference-based operational modal analysis of the full structure through data-driven stochastic subspace identification is presented alongside finite element analysis results for confirmation of modal parameter plausibility. Particular emphasis is placed on the identification and reconstruction of modal response with large contribution from the arch rib web plates.

Spatio-Temporal Field Estimation Using Kriged Kalman Filter (KKF) with Sparsity-Enforcing Sensor Placement.

PubMed

Roy, Venkat; Simonetto, Andrea; Leus, Geert

2018-06-01

We propose a sensor placement method for spatio-temporal field estimation based on a kriged Kalman filter (KKF) using a network of static or mobile sensors. The developed framework dynamically designs the optimal constellation to place the sensors. We combine the estimation error (for the stationary as well as non-stationary component of the field) minimization problem with a sparsity-enforcing penalty to design the optimal sensor constellation in an economic manner. The developed sensor placement method can be directly used for a general class of covariance matrices (ill-conditioned or well-conditioned) modelling the spatial variability of the stationary component of the field, which acts as a correlated observation noise, while estimating the non-stationary component of the field. Finally, a KKF estimator is used to estimate the field using the measurements from the selected sensing locations. Numerical results are provided to exhibit the feasibility of the proposed dynamic sensor placement followed by the KKF estimation method.

Autonomous collection of dynamically-cued multi-sensor imagery

NASA Astrophysics Data System (ADS)

Daniel, Brian; Wilson, Michael L.; Edelberg, Jason; Jensen, Mark; Johnson, Troy; Anderson, Scott

2011-05-01

The availability of imagery simultaneously collected from sensors of disparate modalities enhances an image analyst's situational awareness and expands the overall detection capability to a larger array of target classes. Dynamic cooperation between sensors is increasingly important for the collection of coincident data from multiple sensors either on the same or on different platforms suitable for UAV deployment. Of particular interest is autonomous collaboration between wide area survey detection, high-resolution inspection, and RF sensors that span large segments of the electromagnetic spectrum. The Naval Research Laboratory (NRL) in conjunction with the Space Dynamics Laboratory (SDL) is building sensors with such networked communications capability and is conducting field tests to demonstrate the feasibility of collaborative sensor data collection and exploitation. Example survey / detection sensors include: NuSAR (NRL Unmanned SAR), a UAV compatible synthetic aperture radar system; microHSI, an NRL developed lightweight hyper-spectral imager; RASAR (Real-time Autonomous SAR), a lightweight podded synthetic aperture radar; and N-WAPSS-16 (Nighttime Wide-Area Persistent Surveillance Sensor-16Mpix), a MWIR large array gimbaled system. From these sensors, detected target cues are automatically sent to the NRL/SDL developed EyePod, a high-resolution, narrow FOV EO/IR sensor, for target inspection. In addition to this cooperative data collection, EyePod's real-time, autonomous target tracking capabilities will be demonstrated. Preliminary results and target analysis will be presented.

Multi-sensor calibration of low-cost magnetic, angular rate and gravity systems.

PubMed

Lüken, Markus; Misgeld, Berno J E; Rüschen, Daniel; Leonhardt, Steffen

2015-10-13

We present a new calibration procedure for low-cost nine degrees-of-freedom (9DOF) magnetic, angular rate and gravity (MARG) sensor systems, which relies on a calibration cube, a reference table and a body sensor network (BSN). The 9DOF MARG sensor is part of our recently-developed "Integrated Posture and Activity Network by Medit Aachen" (IPANEMA) BSN. The advantage of this new approach is the use of the calibration cube, which allows for easy integration of two sensor nodes of the IPANEMA BSN. One 9DOF MARG sensor node is thereby used for calibration; the second 9DOF MARG sensor node is used for reference measurements. A novel algorithm uses these measurements to further improve the performance of the calibration procedure by processing arbitrarily-executed motions. In addition, the calibration routine can be used in an alignment procedure to minimize errors in the orientation between the 9DOF MARG sensor system and a motion capture inertial reference system. A two-stage experimental study is conducted to underline the performance of our calibration procedure. In both stages of the proposed calibration procedure, the BSN data, as well as reference tracking data are recorded. In the first stage, the mean values of all sensor outputs are determined as the absolute measurement offset to minimize integration errors in the derived movement model of the corresponding body segment. The second stage deals with the dynamic characteristics of the measurement system where the dynamic deviation of the sensor output compared to a reference system is Sensors 2015, 15 25920 corrected. In practical validation experiments, this procedure showed promising results with a maximum RMS error of 3.89°.

Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare

PubMed Central

Haque, Shah Ahsanul; Rahman, Mustafizur; Aziz, Syed Mahfuzul

2015-01-01

Wireless Sensor Networks (WSN) are vulnerable to various sensor faults and faulty measurements. This vulnerability hinders efficient and timely response in various WSN applications, such as healthcare. For example, faulty measurements can create false alarms which may require unnecessary intervention from healthcare personnel. Therefore, an approach to differentiate between real medical conditions and false alarms will improve remote patient monitoring systems and quality of healthcare service afforded by WSN. In this paper, a novel approach is proposed to detect sensor anomaly by analyzing collected physiological data from medical sensors. The objective of this method is to effectively distinguish false alarms from true alarms. It predicts a sensor value from historic values and compares it with the actual sensed value for a particular instance. The difference is compared against a threshold value, which is dynamically adjusted, to ascertain whether the sensor value is anomalous. The proposed approach has been applied to real healthcare datasets and compared with existing approaches. Experimental results demonstrate the effectiveness of the proposed system, providing high Detection Rate (DR) and low False Positive Rate (FPR). PMID:25884786

A multipath routing protocol based on clustering and ant colony optimization for wireless sensor networks.

PubMed

Yang, Jing; Xu, Mai; Zhao, Wei; Xu, Baoguo

2010-01-01

For monitoring burst events in a kind of reactive wireless sensor networks (WSNs), a multipath routing protocol (MRP) based on dynamic clustering and ant colony optimization (ACO) is proposed. Such an approach can maximize the network lifetime and reduce the energy consumption. An important attribute of WSNs is their limited power supply, and therefore some metrics (such as energy consumption of communication among nodes, residual energy, path length) were considered as very important criteria while designing routing in the MRP. Firstly, a cluster head (CH) is selected among nodes located in the event area according to some parameters, such as residual energy. Secondly, an improved ACO algorithm is applied in the search for multiple paths between the CH and sink node. Finally, the CH dynamically chooses a route to transmit data with a probability that depends on many path metrics, such as energy consumption. The simulation results show that MRP can prolong the network lifetime, as well as balance of energy consumption among nodes and reduce the average energy consumption effectively.

Determination of chlorine concentration using single temperature modulated semiconductor gas sensor

NASA Astrophysics Data System (ADS)

Woźniak, Ł.; Kalinowski, P.; Jasiński, G.; Jasiński, P.

2016-11-01

A periodic temperature modulation using sinusoidal heater voltage was applied to a commercial SnO2 semiconductor gas sensor. Resulting resistance response of the sensor was analyzed using a feature extraction method based on Fast Fourier Transformation (FFT). The amplitudes of the higher harmonics of the FFT from the dynamic nonlinear responses of measured gas were further utilized as an input for Artificial Neuron Network (ANN). Determination of the concentration of chlorine was performed. Moreover, this work evaluates the sensor performance upon sinusoidal temperature modulation.

Design and Implementation of a MAC Protocol for Timely and Reliable Delivery of Command and Data in Dynamic Wireless Sensor Networks

PubMed Central

Oh, Hoon; Van Vinh, Phan

2013-01-01

This paper proposes and implements a new TDMA-based MAC protocol for providing timely and reliable delivery of data and command for monitoring and control networks. In this kind of network, sensor nodes are required to sense data from the monitoring environment periodically and then send the data to a sink. The sink determines whether the environment is safe or not by analyzing the acquired data. Sometimes, a command or control message is sent from the sink to a particular node or a group of nodes to execute the services or request further interested data. The proposed MAC protocol enables bidirectional communication, controls active and sleep modes of a sensor node to conserve energy, and addresses the problem of load unbalancing between the nodes near a sink and the other nodes. It can improve reliability of communication significantly while extending network lifetime. These claims are supported by the experimental results. PMID:24084116

Design and implementation of a MAC protocol for timely and reliable delivery of command and data in dynamic wireless sensor networks.

PubMed

Oh, Hoon; Van Vinh, Phan

2013-09-30

This paper proposes and implements a new TDMA-based MAC protocol for providing timely and reliable delivery of data and command for monitoring and control networks. In this kind of network, sensor nodes are required to sense data from the monitoring environment periodically and then send the data to a sink. The sink determines whether the environment is safe or not by analyzing the acquired data. Sometimes, a command or control message is sent from the sink to a particular node or a group of nodes to execute the services or request further interested data. The proposed MAC protocol enables bidirectional communication, controls active and sleep modes of a sensor node to conserve energy, and addresses the problem of load unbalancing between the nodes near a sink and the other nodes. It can improve reliability of communication significantly while extending network lifetime. These claims are supported by the experimental results.

Load-adaptive practical multi-channel communications in wireless sensor networks.

PubMed

Islam, Md Shariful; Alam, Muhammad Mahbub; Hong, Choong Seon; Lee, Sungwon

2010-01-01

In recent years, a significant number of sensor node prototypes have been designed that provide communications in multiple channels. This multi-channel feature can be effectively exploited to increase the overall capacity and performance of wireless sensor networks (WSNs). In this paper, we present a multi-channel communications system for WSNs that is referred to as load-adaptive practical multi-channel communications (LPMC). LPMC estimates the active load of a channel at the sink since it has a more comprehensive view of the network behavior, and dynamically adds or removes channels based on the estimated load. LPMC updates the routing path to balance the loads of the channels. The nodes in a path use the same channel; therefore, they do not need to switch channels to receive or forward packets. LPMC has been evaluated through extensive simulations, and the results demonstrate that it can effectively increase the delivery ratio, network throughput, and channel utilization, and that it can decrease the end-to-end delay and energy consumption.

Towards Resilient Critical Infrastructures: Application of Type-2 Fuzzy Logic in Embedded Network Security Cyber Sensor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ondrej Linda; Todd Vollmer; Jim Alves-Foss

2011-08-01

Resiliency and cyber security of modern critical infrastructures is becoming increasingly important with the growing number of threats in the cyber-environment. This paper proposes an extension to a previously developed fuzzy logic based anomaly detection network security cyber sensor via incorporating Type-2 Fuzzy Logic (T2 FL). In general, fuzzy logic provides a framework for system modeling in linguistic form capable of coping with imprecise and vague meanings of words. T2 FL is an extension of Type-1 FL which proved to be successful in modeling and minimizing the effects of various kinds of dynamic uncertainties. In this paper, T2 FL providesmore » a basis for robust anomaly detection and cyber security state awareness. In addition, the proposed algorithm was specifically developed to comply with the constrained computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental cyber-security test-bed.« less

Multi-mode clustering model for hierarchical wireless sensor networks

NASA Astrophysics Data System (ADS)

Hu, Xiangdong; Li, Yongfu; Xu, Huifen

2017-03-01

The topology management, i.e., clusters maintenance, of wireless sensor networks (WSNs) is still a challenge due to its numerous nodes, diverse application scenarios and limited resources as well as complex dynamics. To address this issue, a multi-mode clustering model (M2 CM) is proposed to maintain the clusters for hierarchical WSNs in this study. In particular, unlike the traditional time-trigger model based on the whole-network and periodic style, the M2 CM is proposed based on the local and event-trigger operations. In addition, an adaptive local maintenance algorithm is designed for the broken clusters in the WSNs using the spatial-temporal demand changes accordingly. Numerical experiments are performed using the NS2 network simulation platform. Results validate the effectiveness of the proposed model with respect to the network maintenance costs, node energy consumption and transmitted data as well as the network lifetime.

Bridge damage detection using spatiotemporal patterns extracted from dense sensor network

NASA Astrophysics Data System (ADS)

Liu, Chao; Gong, Yongqiang; Laflamme, Simon; Phares, Brent; Sarkar, Soumik

2017-01-01

The alarmingly degrading state of transportation infrastructures combined with their key societal and economic importance calls for automatic condition assessment methods to facilitate smart management of maintenance and repairs. With the advent of ubiquitous sensing and communication capabilities, scalable data-driven approaches is of great interest, as it can utilize large volume of streaming data without requiring detailed physical models that can be inaccurate and computationally expensive to run. Properly designed, a data-driven methodology could enable fast and automatic evaluation of infrastructures, discovery of causal dependencies among various sub-system dynamic responses, and decision making with uncertainties and lack of labeled data. In this work, a spatiotemporal pattern network (STPN) strategy built on symbolic dynamic filtering (SDF) is proposed to explore spatiotemporal behaviors in a bridge network. Data from strain gauges installed on two bridges are generated using finite element simulation for three types of sensor networks from a density perspective (dense, nominal, sparse). Causal relationships among spatially distributed strain data streams are extracted and analyzed for vehicle identification and detection, and for localization of structural degradation in bridges. Multiple case studies show significant capabilities of the proposed approach in: (i) capturing spatiotemporal features to discover causality between bridges (geographically close), (ii) robustness to noise in data for feature extraction, (iii) detecting and localizing damage via comparison of bridge responses to similar vehicle loads, and (iv) implementing real-time health monitoring and decision making work flow for bridge networks. Also, the results demonstrate increased sensitivity in detecting damages and higher reliability in quantifying the damage level with increase in sensor network density.

Moving target tracking through distributed clustering in directional sensor networks.

PubMed

Enayet, Asma; Razzaque, Md Abdur; Hassan, Mohammad Mehedi; Almogren, Ahmad; Alamri, Atif

2014-12-18

The problem of moving target tracking in directional sensor networks (DSNs) introduces new research challenges, including optimal selection of sensing and communication sectors of the directional sensor nodes, determination of the precise location of the target and an energy-efficient data collection mechanism. Existing solutions allow individual sensor nodes to detect the target's location through collaboration among neighboring nodes, where most of the sensors are activated and communicate with the sink. Therefore, they incur much overhead, loss of energy and reduced target tracking accuracy. In this paper, we have proposed a clustering algorithm, where distributed cluster heads coordinate their member nodes in optimizing the active sensing and communication directions of the nodes, precisely determining the target location by aggregating reported sensing data from multiple nodes and transferring the resultant location information to the sink. Thus, the proposed target tracking mechanism minimizes the sensing redundancy and maximizes the number of sleeping nodes in the network. We have also investigated the dynamic approach of activating sleeping nodes on-demand so that the moving target tracking accuracy can be enhanced while maximizing the network lifetime. We have carried out our extensive simulations in ns-3, and the results show that the proposed mechanism achieves higher performance compared to the state-of-the-art works.

Moving Target Tracking through Distributed Clustering in Directional Sensor Networks

PubMed Central

Enayet, Asma; Razzaque, Md. Abdur; Hassan, Mohammad Mehedi; Almogren, Ahmad; Alamri, Atif

2014-01-01

The problem of moving target tracking in directional sensor networks (DSNs) introduces new research challenges, including optimal selection of sensing and communication sectors of the directional sensor nodes, determination of the precise location of the target and an energy-efficient data collection mechanism. Existing solutions allow individual sensor nodes to detect the target's location through collaboration among neighboring nodes, where most of the sensors are activated and communicate with the sink. Therefore, they incur much overhead, loss of energy and reduced target tracking accuracy. In this paper, we have proposed a clustering algorithm, where distributed cluster heads coordinate their member nodes in optimizing the active sensing and communication directions of the nodes, precisely determining the target location by aggregating reported sensing data from multiple nodes and transferring the resultant location information to the sink. Thus, the proposed target tracking mechanism minimizes the sensing redundancy and maximizes the number of sleeping nodes in the network. We have also investigated the dynamic approach of activating sleeping nodes on-demand so that the moving target tracking accuracy can be enhanced while maximizing the network lifetime. We have carried out our extensive simulations in ns-3, and the results show that the proposed mechanism achieves higher performance compared to the state-of-the-art works. PMID:25529205

PG&E's Seismic Network Goes Digital With Strong Motion: Successes and Challenges

NASA Astrophysics Data System (ADS)

Stanton, M. A.; Cullen, J.; McLaren, M. K.

2008-12-01

Pacific Gas and Electric Company (PG&E) is in year 3 of a 5-year project to upgrade the Central Coast Seismic Network (CCSN) from analog to digital. Located along the south-central California coast, the CCSN began operation in 1987, with 20 analog stations; 15 vertical component and 5 dual gain 3-component S-13 sensors. The analog signals travel over FM radio telemetry links and voice channels via PG&E's microwave network to our facility in San Francisco (SF), where the A/D conversion is performed on a computer running Earthworm v7.1, which also transmits the data to the USGS in Menlo Park. At the conversion point the dynamic ranges of the vertical and dual-gain sensors are 40-50dB and 60-70dB, respectively. Dynamic range exceedance (data clipping) generally occurs for a M2.5 or greater event within about 40 km of a station. The motivations to upgrade the seismic network were the need for higher dynamic range and to retire obsolete analog transmission equipment. The upgraded digital stations consist of the existing velocity sensors, a 131A-02/3 accelerometer and a Reftek 130-01 Broadband Seismic Recorder for digital data recording and transmission to SF. Vertical only stations have one component of velocity and 3 components of acceleration. Dual gain sites have 3 components of velocity and 3 of acceleration. To date we have successfully upgraded 6 sites; 3 more will be installed by the end of 2008. Some of the advantages of going digital are 1) data is recorded at each site and in SF, 2) substantially increased dynamic range of the velocity sensors to 120dB, as observed by on scale, close-by recordings from a M3.9 San Simeon aftershock on 04/29/2008, 3) accelerometers for on scale recording of large earthquakes, and 4) ability to contribute our strong motion data to USGS ShakeMaps. A significant challenge has been consistent radio communications. To resolve this issue we are installing point-to-multipoint Motorola Canopy spread spectrum radios at the stations and communication towers.

Percutaneous window chamber method for chronic intravital microscopy of sensor-tissue interactions.

PubMed

Koschwanez, Heidi E; Klitzman, Bruce; Reichert, W Monty

2008-11-01

A dorsal, two-sided skin-fold window chamber model was employed previously by Gough in glucose sensor research to characterize poorly understood physiological factors affecting sensor performance. We have extended this work by developing a percutaneous one-sided window chamber model for the rodent dorsum that offers both a larger subcutaneous area and a less restrictive tissue space than previous animal models. A surgical procedure for implanting a sensor into the subcutis beneath an acrylic window (15 mm diameter) is presented. Methods to quantify changes in the microvascular network and red blood cell perfusion around the sensors using noninvasive intravital microscopy and laser Doppler flowmetry are described. The feasibility of combining interstitial glucose monitoring from an implanted sensor with intravital fluorescence microscopy was explored using a bolus injection of fluorescein and dextrose to observe real-time mass transport of a small molecule at the sensor-tissue interface. The percutaneous window chamber provides an excellent model for assessing the influence of different sensor modifications, such as surface morphologies, on neovascularization using real-time monitoring of the microvascular network and tissue perfusion. However, the tissue response to an implanted sensor was variable, and some sensors migrated entirely out of the field of view and could not be observed adequately. A percutaneous optical window provides direct, real-time images of the development and dynamics of microvascular networks, microvessel patency, and fibrotic encapsulation at the tissue-sensor interface. Additionally, observing microvessels following combined bolus injections of a fluorescent dye and glucose in the local sensor environment demonstrated a valuable technique to visualize mass transport at the sensor surface.

Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

NASA Technical Reports Server (NTRS)

Jorgensen, Charles C.

1997-01-01

A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.

Managed traffic evacuation using distributed sensor processing

NASA Astrophysics Data System (ADS)

Ramuhalli, Pradeep; Biswas, Subir

2005-05-01

This paper presents an integrated sensor network and distributed event processing architecture for managed in-building traffic evacuation during natural and human-caused disasters, including earthquakes, fire and biological/chemical terrorist attacks. The proposed wireless sensor network protocols and distributed event processing mechanisms offer a new distributed paradigm for improving reliability in building evacuation and disaster management. The networking component of the system is constructed using distributed wireless sensors for measuring environmental parameters such as temperature, humidity, and detecting unusual events such as smoke, structural failures, vibration, biological/chemical or nuclear agents. Distributed event processing algorithms will be executed by these sensor nodes to detect the propagation pattern of the disaster and to measure the concentration and activity of human traffic in different parts of the building. Based on this information, dynamic evacuation decisions are taken for maximizing the evacuation speed and minimizing unwanted incidents such as human exposure to harmful agents and stampedes near exits. A set of audio-visual indicators and actuators are used for aiding the automated evacuation process. In this paper we develop integrated protocols, algorithms and their simulation models for the proposed sensor networking and the distributed event processing framework. Also, efficient harnessing of the individually low, but collectively massive, processing abilities of the sensor nodes is a powerful concept behind our proposed distributed event processing algorithms. Results obtained through simulation in this paper are used for a detailed characterization of the proposed evacuation management system and its associated algorithmic components.

Analog neural network control method proposed for use in a backup satellite control mode

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frigo, J.R.; Tilden, M.W.

1998-03-01

The authors propose to use an analog neural network controller implemented in hardware, independent of the active control system, for use in a satellite backup control mode. The controller uses coarse sun sensor inputs. The field of view of the sensors activate the neural controller, creating an analog dead band with respect to the direction of the sun on each axis. This network controls the orientation of the vehicle toward the sunlight to ensure adequate power for the system. The attitude of the spacecraft is stabilized with respect to the ambient magnetic field on orbit. This paper develops a modelmore » of the controller using real-time coarse sun sensor data and a dynamic model of a prototype system based on a satellite system. The simulation results and the feasibility of this control method for use in a satellite backup control mode are discussed.« less

Structural health monitoring using wireless sensor networks

NASA Astrophysics Data System (ADS)

Sreevallabhan, K.; Nikhil Chand, B.; Ramasamy, Sudha

2017-11-01

Monitoring and analysing health of large structures like bridges, dams, buildings and heavy machinery is important for safety, economical, operational, making prior protective measures, and repair and maintenance point of view. In recent years there is growing demand for such larger structures which in turn make people focus more on safety. By using Microelectromechanical Systems (MEMS) Accelerometer we can perform Structural Health Monitoring by studying the dynamic response through measure of ambient vibrations and strong motion of such structures. By using Wireless Sensor Networks (WSN) we can embed these sensors in wireless networks which helps us to transmit data wirelessly thus we can measure the data wirelessly at any remote location. This in turn reduces heavy wiring which is a cost effective as well as time consuming process to lay those wires. In this paper we developed WSN based MEMS-accelerometer for Structural to test the results in the railway bridge near VIT University, Vellore campus.

Location identification for indoor instantaneous point contaminant source by probability-based inverse Computational Fluid Dynamics modeling.

PubMed

Liu, X; Zhai, Z

2008-02-01

Indoor pollutions jeopardize human health and welfare and may even cause serious morbidity and mortality under extreme conditions. To effectively control and improve indoor environment quality requires immediate interpretation of pollutant sensor readings and accurate identification of indoor pollution history and source characteristics (e.g. source location and release time). This procedure is complicated by non-uniform and dynamic contaminant indoor dispersion behaviors as well as diverse sensor network distributions. This paper introduces a probability concept based inverse modeling method that is able to identify the source location for an instantaneous point source placed in an enclosed environment with known source release time. The study presents the mathematical models that address three different sensing scenarios: sensors without concentration readings, sensors with spatial concentration readings, and sensors with temporal concentration readings. The paper demonstrates the inverse modeling method and algorithm with two case studies: air pollution in an office space and in an aircraft cabin. The predictions were successfully verified against the forward simulation settings, indicating good capability of the method in finding indoor pollutant sources. The research lays a solid ground for further study of the method for more complicated indoor contamination problems. The method developed can help track indoor contaminant source location with limited sensor outputs. This will ensure an effective and prompt execution of building control strategies and thus achieve a healthy and safe indoor environment. The method can also assist the design of optimal sensor networks.

LESS: Link Estimation with Sparse Sampling in Intertidal WSNs

PubMed Central

Ji, Xiaoyu; Chen, Yi-chao; Li, Xiaopeng; Xu, Wenyuan

2018-01-01

Deploying wireless sensor networks (WSN) in the intertidal area is an effective approach for environmental monitoring. To sustain reliable data delivery in such a dynamic environment, a link quality estimation mechanism is crucial. However, our observations in two real WSN systems deployed in the intertidal areas reveal that link update in routing protocols often suffers from energy and bandwidth waste due to the frequent link quality measurement and updates. In this paper, we carefully investigate the network dynamics using real-world sensor network data and find it feasible to achieve accurate estimation of link quality using sparse sampling. We design and implement a compressive-sensing-based link quality estimation protocol, LESS, which incorporates both spatial and temporal characteristics of the system to aid the link update in routing protocols. We evaluate LESS in both real WSN systems and a large-scale simulation, and the results show that LESS can reduce energy and bandwidth consumption by up to 50% while still achieving more than 90% link quality estimation accuracy. PMID:29494557

A Social Potential Fields Approach for Self-Deployment and Self-Healing in Hierarchical Mobile Wireless Sensor Networks

PubMed Central

González-Parada, Eva; Cano-García, Jose; Aguilera, Francisco; Sandoval, Francisco; Urdiales, Cristina

2017-01-01

Autonomous mobile nodes in mobile wireless sensor networks (MWSN) allow self-deployment and self-healing. In both cases, the goals are: (i) to achieve adequate coverage; and (ii) to extend network life. In dynamic environments, nodes may use reactive algorithms so that each node locally decides when and where to move. This paper presents a behavior-based deployment and self-healing algorithm based on the social potential fields algorithm. In the proposed algorithm, nodes are attached to low cost robots to autonomously navigate in the coverage area. The proposed algorithm has been tested in environments with and without obstacles. Our study also analyzes the differences between non-hierarchical and hierarchical routing configurations in terms of network life and coverage. PMID:28075364

A Social Potential Fields Approach for Self-Deployment and Self-Healing in Hierarchical Mobile Wireless Sensor Networks.

PubMed

González-Parada, Eva; Cano-García, Jose; Aguilera, Francisco; Sandoval, Francisco; Urdiales, Cristina

2017-01-09

Autonomous mobile nodes in mobile wireless sensor networks (MWSN) allow self-deployment and self-healing. In both cases, the goals are: (i) to achieve adequate coverage; and (ii) to extend network life. In dynamic environments, nodes may use reactive algorithms so that each node locally decides when and where to move. This paper presents a behavior-based deployment and self-healing algorithm based on the social potential fields algorithm. In the proposed algorithm, nodes are attached to low cost robots to autonomously navigate in the coverage area. The proposed algorithm has been tested in environments with and without obstacles. Our study also analyzes the differences between non-hierarchical and hierarchical routing configurations in terms of network life and coverage.

Intelligent Wireless Sensor Networks for System Health Monitoring

NASA Technical Reports Server (NTRS)

Alena, Rick

2011-01-01

Wireless sensor networks (WSN) based on the IEEE 802.15.4 Personal Area Network (PAN) standard are finding increasing use in the home automation and emerging smart energy markets. The network and application layers, based on the ZigBee 2007 Standard, provide a convenient framework for component-based software that supports customer solutions from multiple vendors. WSNs provide the inherent fault tolerance required for aerospace applications. The Discovery and Systems Health Group at NASA Ames Research Center has been developing WSN technology for use aboard aircraft and spacecraft for System Health Monitoring of structures and life support systems using funding from the NASA Engineering and Safety Center and Exploration Technology Development and Demonstration Program. This technology provides key advantages for low-power, low-cost ancillary sensing systems particularly across pressure interfaces and in areas where it is difficult to run wires. Intelligence for sensor networks could be defined as the capability of forming dynamic sensor networks, allowing high-level application software to identify and address any sensor that joined the network without the use of any centralized database defining the sensors characteristics. The IEEE 1451 Standard defines methods for the management of intelligent sensor systems and the IEEE 1451.4 section defines Transducer Electronic Datasheets (TEDS), which contain key information regarding the sensor characteristics such as name, description, serial number, calibration information and user information such as location within a vehicle. By locating the TEDS information on the wireless sensor itself and enabling access to this information base from the application software, the application can identify the sensor unambiguously and interpret and present the sensor data stream without reference to any other information. The application software is able to read the status of each sensor module, responding in real-time to changes of PAN configuration, providing the appropriate response for maintaining overall sensor system function, even when sensor modules fail or the WSN is reconfigured. The session will present the architecture and technical feasibility of creating fault-tolerant WSNs for aerospace applications based on our application of the technology to a Structural Health Monitoring testbed. The interim results of WSN development and testing including our software architecture for intelligent sensor management will be discussed in the context of the specific tradeoffs required for effective use. Initial certification measurement techniques and test results gauging WSN susceptibility to Radio Frequency interference are introduced as key challenges for technology adoption. A candidate Developmental and Flight Instrumentation implementation using intelligent sensor networks for wind tunnel and flight tests is developed as a guide to understanding key aspects of the aerospace vehicle design, test and operations life cycle.

Multi-Sensor Calibration of Low-Cost Magnetic, Angular Rate and Gravity Systems

PubMed Central

Lüken, Markus; Misgeld, Berno J.E.; Rüschen, Daniel; Leonhardt, Steffen

2015-01-01

We present a new calibration procedure for low-cost nine degrees-of-freedom (9DOF) magnetic, angular rate and gravity (MARG) sensor systems, which relies on a calibration cube, a reference table and a body sensor network (BSN). The 9DOF MARG sensor is part of our recently-developed “Integrated Posture and Activity Network by Medit Aachen” (IPANEMA) BSN. The advantage of this new approach is the use of the calibration cube, which allows for easy integration of two sensor nodes of the IPANEMA BSN. One 9DOF MARG sensor node is thereby used for calibration; the second 9DOF MARG sensor node is used for reference measurements. A novel algorithm uses these measurements to further improve the performance of the calibration procedure by processing arbitrarily-executed motions. In addition, the calibration routine can be used in an alignment procedure to minimize errors in the orientation between the 9DOF MARG sensor system and a motion capture inertial reference system. A two-stage experimental study is conducted to underline the performance of our calibration procedure. In both stages of the proposed calibration procedure, the BSN data, as well as reference tracking data are recorded. In the first stage, the mean values of all sensor outputs are determined as the absolute measurement offset to minimize integration errors in the derived movement model of the corresponding body segment. The second stage deals with the dynamic characteristics of the measurement system where the dynamic deviation of the sensor output compared to a reference system is corrected. In practical validation experiments, this procedure showed promising results with a maximum RMS error of 3.89°. PMID:26473873

Cluster Cooperation in Wireless-Powered Sensor Networks: Modeling and Performance Analysis.

PubMed

Zhang, Chao; Zhang, Pengcheng; Zhang, Weizhan

2017-09-27

A wireless-powered sensor network (WPSN) consisting of one hybrid access point (HAP), a near cluster and the corresponding far cluster is investigated in this paper. These sensors are wireless-powered and they transmit information by consuming the harvested energy from signal ejected by the HAP. Sensors are able to harvest energy as well as store the harvested energy. We propose that if sensors in near cluster do not have their own information to transmit, acting as relays, they can help the sensors in a far cluster to forward information to the HAP in an amplify-and-forward (AF) manner. We use a finite Markov chain to model the dynamic variation process of the relay battery, and give a general analyzing model for WPSN with cluster cooperation. Though the model, we deduce the closed-form expression for the outage probability as the metric of this network. Finally, simulation results validate the start point of designing this paper and correctness of theoretical analysis and show how parameters have an effect on system performance. Moreover, it is also known that the outage probability of sensors in far cluster can be drastically reduced without sacrificing the performance of sensors in near cluster if the transmit power of HAP is fairly high. Furthermore, in the aspect of outage performance of far cluster, the proposed scheme significantly outperforms the direct transmission scheme without cooperation.

Cluster Cooperation in Wireless-Powered Sensor Networks: Modeling and Performance Analysis

PubMed Central

Zhang, Chao; Zhang, Pengcheng; Zhang, Weizhan

2017-01-01

A wireless-powered sensor network (WPSN) consisting of one hybrid access point (HAP), a near cluster and the corresponding far cluster is investigated in this paper. These sensors are wireless-powered and they transmit information by consuming the harvested energy from signal ejected by the HAP. Sensors are able to harvest energy as well as store the harvested energy. We propose that if sensors in near cluster do not have their own information to transmit, acting as relays, they can help the sensors in a far cluster to forward information to the HAP in an amplify-and-forward (AF) manner. We use a finite Markov chain to model the dynamic variation process of the relay battery, and give a general analyzing model for WPSN with cluster cooperation. Though the model, we deduce the closed-form expression for the outage probability as the metric of this network. Finally, simulation results validate the start point of designing this paper and correctness of theoretical analysis and show how parameters have an effect on system performance. Moreover, it is also known that the outage probability of sensors in far cluster can be drastically reduced without sacrificing the performance of sensors in near cluster if the transmit power of HAP is fairly high. Furthermore, in the aspect of outage performance of far cluster, the proposed scheme significantly outperforms the direct transmission scheme without cooperation. PMID:28953231

Cacades: A reliable dissemination protocol for data collection sensor network

USGS Publications Warehouse

Peng, Y.; Song, W.; Huang, R.; Xu, M.; Shirazi, B.; LaHusen, R.; Pei, G.

2009-01-01

In this paper, we propose a fast and reliable data dissemination protocol Cascades to disseminate data from the sink(base station) to all or a subset of nodes in a data collection sensor network. Cascades makes use of the parentmonitor-children analogy to ensure reliable dissemination. Each node monitors whether or not its children have received the broadcast messages through snooping children's rebroadcasts or waiting for explicit ACKs. If a node detects a gap in its message sequences, it can fetch the missing messages from its neighbours reactively. Cascades also considers many practical issues for field deployment, such as dynamic topology, link/node failure, etc.. It therefore guarantees that a disseminated message from the sink will reach all intended receivers and the dissemination is terminated in a short time period. Notice that, all existing dissemination protocols either do not guarantee reliability or do not terminate [1, 2], which does not meet the requirement of real-time command control. We conducted experiment evaluations in both TOSSIM simulator and a sensor network testbed to compare Cascades with those existing dissemination protocols in TinyOS sensor networks, which show that Cascades achieves a higher degree of reliability, lower communication cost, and less delivery delay. ??2009 IEEE.

Data fusion for target tracking and classification with wireless sensor network

NASA Astrophysics Data System (ADS)

Pannetier, Benjamin; Doumerc, Robin; Moras, Julien; Dezert, Jean; Canevet, Loic

2016-10-01

In this paper, we address the problem of multiple ground target tracking and classification with information obtained from a unattended wireless sensor network. A multiple target tracking (MTT) algorithm, taking into account road and vegetation information, is proposed based on a centralized architecture. One of the key issue is how to adapt classical MTT approach to satisfy embedded processing. Based on track statistics, the classification algorithm uses estimated location, velocity and acceleration to help to classify targets. The algorithms enables tracking human and vehicles driving both on and off road. We integrate road or trail width and vegetation cover, as constraints in target motion models to improve performance of tracking under constraint with classification fusion. Our algorithm also presents different dynamic models, to palliate the maneuvers of targets. The tracking and classification algorithms are integrated into an operational platform (the fusion node). In order to handle realistic ground target tracking scenarios, we use an autonomous smart computer deposited in the surveillance area. After the calibration step of the heterogeneous sensor network, our system is able to handle real data from a wireless ground sensor network. The performance of system is evaluated in a real exercise for intelligence operation ("hunter hunt" scenario).

Wearable sensors for human health monitoring

NASA Astrophysics Data System (ADS)

Asada, H. Harry; Reisner, Andrew

2006-03-01

Wearable sensors for continuous monitoring of vital signs for extended periods of weeks or months are expected to revolutionize healthcare services in the home and workplace as well as in hospitals and nursing homes. This invited paper describes recent research progress in wearable health monitoring technology and its clinical applications, with emphasis on blood pressure and circulatory monitoring. First, a finger ring-type wearable blood pressure sensor based on photo plethysmogram is presented. Technical issues, including motion artifact reduction, power saving, and wearability enhancement, will be addressed. Second, sensor fusion and sensor networking for integrating multiple sensors with diverse modalities will be discussed for comprehensive monitoring and diagnosis of health status. Unlike traditional snap-shot measurements, continuous monitoring with wearable sensors opens up the possibility to treat the physiological system as a dynamical process. This allows us to apply powerful system dynamics and control methodologies, such as adaptive filtering, single- and multi-channel system identification, active noise cancellation, and adaptive control, to the monitoring and treatment of highly complex physiological systems. A few clinical trials illustrate the potentials of the wearable sensor technology for future heath care services.

Optimal dynamic voltage scaling for wireless sensor nodes with real-time constraints

NASA Astrophysics Data System (ADS)

Cassandras, Christos G.; Zhuang, Shixin

2005-11-01

Sensors are increasingly embedded in manufacturing systems and wirelessly networked to monitor and manage operations ranging from process and inventory control to tracking equipment and even post-manufacturing product monitoring. In building such sensor networks, a critical issue is the limited and hard to replenish energy in the devices involved. Dynamic voltage scaling is a technique that controls the operating voltage of a processor to provide desired performance while conserving energy and prolonging the overall network's lifetime. We consider such power-limited devices processing time-critical tasks which are non-preemptive, aperiodic and have uncertain arrival times. We treat voltage scaling as a dynamic optimization problem whose objective is to minimize energy consumption subject to hard or soft real-time execution constraints. In the case of hard constraints, we build on prior work (which engages a voltage scaling controller at task completion times) by developing an intra-task controller that acts at all arrival times of incoming tasks. We show that this optimization problem can be decomposed into two simpler ones whose solution leads to an algorithm that does not actually require solving any nonlinear programming problems. In the case of soft constraints, this decomposition must be partly relaxed, but it still leads to a scalable (linear in the number of tasks) algorithm. Simulation results are provided to illustrate performance improvements in systems with intra-task controllers compared to uncontrolled systems or those using inter-task control.

Intelligent Sensing in Dynamic Environments Using Markov Decision Process

PubMed Central

Nanayakkara, Thrishantha; Halgamuge, Malka N.; Sridhar, Prasanna; Madni, Asad M.

2011-01-01

In a network of low-powered wireless sensors, it is essential to capture as many environmental events as possible while still preserving the battery life of the sensor node. This paper focuses on a real-time learning algorithm to extend the lifetime of a sensor node to sense and transmit environmental events. A common method that is generally adopted in ad-hoc sensor networks is to periodically put the sensor nodes to sleep. The purpose of the learning algorithm is to couple the sensor’s sleeping behavior to the natural statistics of the environment hence that it can be in optimal harmony with changes in the environment, the sensors can sleep when steady environment and stay awake when turbulent environment. This paper presents theoretical and experimental validation of a reward based learning algorithm that can be implemented on an embedded sensor. The key contribution of the proposed approach is the design and implementation of a reward function that satisfies a trade-off between the above two mutually contradicting objectives, and a linear critic function to approximate the discounted sum of future rewards in order to perform policy learning. PMID:22346624

Temperature-Dependent Conformational Properties of Human Neuronal Calcium Sensor-1 Protein Revealed by All-Atom Simulations.

PubMed

Zhu, Yuzhen; Ma, Buyong; Qi, Ruxi; Nussinov, Ruth; Zhang, Qingwen

2016-04-14

Neuronal calcium sensor-1 (NCS-1) protein has orthologues from Saccharomyces cerevisiae to human with highly conserved amino acid sequences. NCS-1 is an important factor controlling the animal's response to temperature change. This leads us to investigate the temperature effects on the conformational dynamics of human NCS-1 at 310 and 316 K by all-atom molecular dynamics (MD) simulations and dynamic community network analysis. Four independent 500 ns MD simulations show that secondary structure content at 316 K is similar to that at 310 K, whereas the global protein structure is expanded. Loop 3 (L3) adopts an extended state occuping the hydrophobic crevice, and the number of suboptimal communication paths between residue D176 and V190 is reduced at 316 K. The dynamic community network analysis suggests that the interdomain correlation is weakened, and the intradomain coupling is strengthened at 316 K. The elevated temperature reduces the number of the salt bridges, especially in C-domain. This study suggests that the elevated temperature affects the conformational dynamics of human NCS-1 protein. Comparison of the structural dynamics of R102Q mutant and Δ176-190 truncated NCS-1 suggests that the structural and dynamical response of NCS-1 protein to elevated temperature may be one of its intrinsic functional properties.

A Fuzzy-Based Approach for Sensing, Coding and Transmission Configuration of Visual Sensors in Smart City Applications

PubMed Central

Costa, Daniel G.; Collotta, Mario; Pau, Giovanni; Duran-Faundez, Cristian

2017-01-01

The advance of technologies in several areas has allowed the development of smart city applications, which can improve the way of life in modern cities. When employing visual sensors in that scenario, still images and video streams may be retrieved from monitored areas, potentially providing valuable data for many applications. Actually, visual sensor networks may need to be highly dynamic, reflecting the changing of parameters in smart cities. In this context, characteristics of visual sensors and conditions of the monitored environment, as well as the status of other concurrent monitoring systems, may affect how visual sensors collect, encode and transmit information. This paper proposes a fuzzy-based approach to dynamically configure the way visual sensors will operate concerning sensing, coding and transmission patterns, exploiting different types of reference parameters. This innovative approach can be considered as the basis for multi-systems smart city applications based on visual monitoring, potentially bringing significant results for this research field. PMID:28067777

A Fuzzy-Based Approach for Sensing, Coding and Transmission Configuration of Visual Sensors in Smart City Applications.

PubMed

Costa, Daniel G; Collotta, Mario; Pau, Giovanni; Duran-Faundez, Cristian

2017-01-05

The advance of technologies in several areas has allowed the development of smart city applications, which can improve the way of life in modern cities. When employing visual sensors in that scenario, still images and video streams may be retrieved from monitored areas, potentially providing valuable data for many applications. Actually, visual sensor networks may need to be highly dynamic, reflecting the changing of parameters in smart cities. In this context, characteristics of visual sensors and conditions of the monitored environment, as well as the status of other concurrent monitoring systems, may affect how visual sensors collect, encode and transmit information. This paper proposes a fuzzy-based approach to dynamically configure the way visual sensors will operate concerning sensing, coding and transmission patterns, exploiting different types of reference parameters. This innovative approach can be considered as the basis for multi-systems smart city applications based on visual monitoring, potentially bringing significant results for this research field.

Realtime Delivery of Alarms and Key Observables in a Deployed Hydrological Sensor Network

NASA Astrophysics Data System (ADS)

Marshall, I. W.; Price, M. C.; Li, H.; Boyd, N.; Boult, S.

2007-12-01

It has widely [1-3] been proposed that sensor networks are a good solution for environmental monitoring. However, this application presents a number of major challenges for current technology. In particular environmental science involves the study of coupled non-equilibrium dynamic processes that generate time series with non-stationary means and strongly dependent variables and which operate in the presence of large amounts of noise/interference (thermal, chemical and biological) and multiple quasi-periodic forcing factors (diurnal cycles, tides, etc). This typically means that any analysis must be based on large data samples obtained at multiple scales of space and time. In addition the areas of interest are large, relatively inaccessible and typically extremely hostile to electronic instrumentation. Our analysis of these factors has encouraged us to focus on this list of generic requirements; a) Node lifetime (between visits) should be 1 yr or greater b) Communication range should be ~250m c) Nodes should be portable, unobtrusive, low cost, etc. d) Networks are expected to be sparse since areas of interest are large and budgets are small However, the characteristics of each environment, the dominant processes operating in it and the measurements that are of interest are sufficiently different that the design of an appropriate sensor network solution is normally most determined by site specific constraints. Most importantly the opportunities for exploiting contextual correlation to disambiguate observations and improve the maintenance and robustness of a deployed sensor network are always site specific. We will describe the design and initial deployment of a hydrological sensor network we are developing to assess the hydro-dynamics of surface water drainage into Great Crowden Brook in the Peak District (UK). The complete network will observe soil moisture, temperature and rainfall on a number of transects across the valley, and will also investigate water quality parameters (colouration, turbidity, Ph) in the stream. GSM access for remote real time reporting of network status is only available from the hilltops so a multihop communication strategy is being used for communication from the valley floor. To minimise radio usage and maximise battery life we are reporting only those alarms and events that are judged to be of high priority by embedding a simple rule based decision engine in each node. The rule conditions are derived from spatio-temporal cross-correlation of the available sensor inputs. . We report on our initial experiments with correlating readings for management purposes, and offer some initial hypotheses regarding aspects of this that might be generic based on a comparison with data obtained in an earlier experiment in a marine setting [4]. 1. http://cens.ucla.edu/ 2. http://eyes.eu.org/ 3. http://www.ee.unimelb.edu.au/ISSNIP/ 4. J. Tateson, C. Roadknight, A. Gonzalez, T. Khan, S. Fitz, I. Henning, N. Boyd, C. Vincent, and I. W. Marshall. Real World Issues in Deploying a Wireless Sensor Network. In Workshop on Real-World Wireless Sensor Networks REALWSN'05, Stockholm, Sweden, June 2005

Emerging technology for advancing the treatment of epilepsy using a dynamic control framework.

PubMed

Stanslaski, Scott; Giftakis, John; Stypulkowski, Paul; Carlson, Dave; Afshar, Pedram; Cong, Peng; Denison, Timothy

2011-01-01

We briefly describe a dynamic control system framework for neuromodulation for epilepsy, with an emphasis on its practical challenges and the preliminary validation of key prototype technologies in a chronic animal model. The current state of neuromodulation can be viewed as a classical dynamic control framework such that the nervous system is the classical "plant", the neural stimulator is the controller/actuator, clinical observation, patient diaries and/or measured bio-markers are the sensor, and clinical judgment applied to these sensor inputs forms the state estimator. Technology can potentially address two main factors contributing to the performance limitations of existing systems: "observability," the ability to observe the state of the system from output measurements, and "controllability," the ability to drive the system to a desired state. In addition to improving sensors and actuator performance, methods and tools to better understand disease state dynamics and state estimation are also critical for improving therapy outcomes. We describe our preliminary validation of key "observability" and "controllability" technology blocks using an implanted research tool in an epilepsy disease model. This model allows for testing the key emerging technologies in a representative neural network of therapeutic importance. In the future, we believe these technologies might enable both first principles understanding of neural network behavior for optimizing therapy design, and provide a practical pathway towards clinical translation.

Robust fault detection of wind energy conversion systems based on dynamic neural networks.

PubMed

Talebi, Nasser; Sadrnia, Mohammad Ali; Darabi, Ahmad

2014-01-01

Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.

Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks

PubMed Central

Talebi, Nasser; Sadrnia, Mohammad Ali; Darabi, Ahmad

2014-01-01

Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate. PMID:24744774

An improved genetic algorithm for increasing the addressing accuracy of encoding fiber Bragg grating sensor network

NASA Astrophysics Data System (ADS)

Liu, Huanlin; Wang, Chujun; Chen, Yong

2018-01-01

Large-capacity encoding fiber Bragg grating (FBG) sensor network is widely used in modern long-term health monitoring system. Encoding FBG sensors have greatly improved the capacity of distributed FBG sensor network. However, the error of addressing increases correspondingly with the enlarging of capacity. To address the issue, an improved algorithm called genetic tracking algorithm (GTA) is proposed in the paper. In the GTA, for improving the success rate of matching and reducing the large number of redundant matching operations generated by sequential matching, the individuals are designed based on the feasible matching. Then, two kinds of self-crossover ways and a dynamic variation during mutation process are designed to increase the diversity of individuals and to avoid falling into local optimum. Meanwhile, an assistant decision is proposed to handle the issue that the GTA cannot solve when the variation of sensor information is highly overlapped. The simulation results indicate that the proposed GTA has higher accuracy compared with the traditional tracking algorithm and the enhanced tracking algorithm. In order to address the problems of spectrum fragmentation and low sharing degree of spectrum resources in survivable.

Analytical approach to cross-layer protocol optimization in wireless sensor networks

NASA Astrophysics Data System (ADS)

Hortos, William S.

2008-04-01

In the distributed operations of route discovery and maintenance, strong interaction occurs across mobile ad hoc network (MANET) protocol layers. Quality of service (QoS) requirements of multimedia service classes must be satisfied by the cross-layer protocol, along with minimization of the distributed power consumption at nodes and along routes to battery-limited energy constraints. In previous work by the author, cross-layer interactions in the MANET protocol are modeled in terms of a set of concatenated design parameters and associated resource levels by multivariate point processes (MVPPs). Determination of the "best" cross-layer design is carried out using the optimal control of martingale representations of the MVPPs. In contrast to the competitive interaction among nodes in a MANET for multimedia services using limited resources, the interaction among the nodes of a wireless sensor network (WSN) is distributed and collaborative, based on the processing of data from a variety of sensors at nodes to satisfy common mission objectives. Sensor data originates at the nodes at the periphery of the WSN, is successively transported to other nodes for aggregation based on information-theoretic measures of correlation and ultimately sent as information to one or more destination (decision) nodes. The "multimedia services" in the MANET model are replaced by multiple types of sensors, e.g., audio, seismic, imaging, thermal, etc., at the nodes; the QoS metrics associated with MANETs become those associated with the quality of fused information flow, i.e., throughput, delay, packet error rate, data correlation, etc. Significantly, the essential analytical approach to MANET cross-layer optimization, now based on the MVPPs for discrete random events occurring in the WSN, can be applied to develop the stochastic characteristics and optimality conditions for cross-layer designs of sensor network protocols. Functional dependencies of WSN performance metrics are described in terms of the concatenated protocol parameters. New source-to-destination routes are sought that optimize cross-layer interdependencies to achieve the "best available" performance in the WSN. The protocol design, modified from a known reactive protocol, adapts the achievable performance to the transient network conditions and resource levels. Control of network behavior is realized through the conditional rates of the MVPPs. Optimal cross-layer protocol parameters are determined by stochastic dynamic programming conditions derived from models of transient packetized sensor data flows. Moreover, the defining conditions for WSN configurations, grouping sensor nodes into clusters and establishing data aggregation at processing nodes within those clusters, lead to computationally tractable solutions to the stochastic differential equations that describe network dynamics. Closed-form solution characteristics provide an alternative to the "directed diffusion" methods for resource-efficient WSN protocols published previously by other researchers. Performance verification of the resulting cross-layer designs is found by embedding the optimality conditions for the protocols in actual WSN scenarios replicated in a wireless network simulation environment. Performance tradeoffs among protocol parameters remain for a sequel to the paper.

Neurodynamics in the Sensorimotor Loop: Representing Behavior Relevant External Situations

PubMed Central

Pasemann, Frank

2017-01-01

In the context of the dynamical system approach to cognition and supposing that brains or brain-like systems controlling the behavior of autonomous systems are permanently driven by their sensor signals, the paper approaches the question of neurodynamics in the sensorimotor loop in a purely formal way. This is carefully done by addressing the problem in three steps, using the time-discrete dynamics of standard neural networks and a fiber space representation for better clearness. Furthermore, concepts like meta-transients, parametric stability and dynamical forms are introduced, where meta-transients describe the effect of realistic sensor inputs, parametric stability refers to a class of sensor inputs all generating the “same type” of dynamic behavior, and a dynamical form comprises the corresponding class of parametrized dynamical systems. It is argued that dynamical forms are the essential internal representatives of behavior relevant external situations. Consequently, it is suggested that dynamical forms are the basis for a memory of these situations. Finally, based on the observation that not all brain process have a direct effect on the motor activity, a natural splitting of neurodynamics into vertical (internal) and horizontal (effective) parts is introduced. PMID:28217092

Exploring the impact of big data in economic geology using cloud-based synthetic sensor networks

NASA Astrophysics Data System (ADS)

Klump, J. F.; Robertson, J.

2015-12-01

In a market demanding lower resource prices and increasing efficiencies, resources companies are increasingly looking to the realm of real-time, high-frequency data streams to better measure and manage their minerals processing chain, from pit to plant to port. Sensor streams can include real-time drilling engineering information, data streams from mining trucks, and on-stream sensors operating in the plant feeding back rich chemical information. There are also many opportunities to deploy new sensor streams - unlike environmental monitoring networks, the mine environment is not energy- or bandwidth-limited. Although the promised efficiency dividends are inviting, the path to achieving these is difficult to see for most companies. As well as knowing where to invest in new sensor technology and how to integrate the new data streams, companies must grapple with risk-laden changes to their established methods of control to achieve maximum gains. What is required is a sandbox data environment for the development of analysis and control strategies at scale, allowing companies to de-risk proposed changes before actually deploying them to a live mine environment. In this presentation we describe our approach to simulating real-time scaleable data streams in a mine environment. Our sandbox consists of three layers: (a) a ground-truth layer that contains geological models, which can be statistically based on historical operations data, (b) a measurement layer - a network of RESTful synthetic sensor microservices which can simulate measurements of ground-truth properties, and (c) a control layer, which integrates the sensor streams and drives the measurement and optimisation strategies. The control layer could be a new machine learner, or simply a company's existing data infrastructure. Containerisation allows rapid deployment of large numbers of sensors, as well as service discovery to form a dynamic network of thousands of sensors, at a far lower cost than physically building the network.

If it walks like a duck: nanosensor threat assessment

NASA Astrophysics Data System (ADS)

Chachis, George C.

2003-09-01

A convergence of technologies is making deployment of unattended ground nanosensors operationally feasible in terms of energy, communications for both arbitrated and self-organizing distributed, collective behaviors. A number of nano communications technologies are already making network-centric systems possible for MicroElectrical Mechanical (MEM) sensor devices today. Similar technologies may make NanoElectrical Mechanical (NEM) sensor devices operationally feasible a few years from now. Just as organizational behaviors of large numbers of nanodevices can derive strategies from social insects and other group-oriented animals, bio-inspired heuristics for threat assessment provide a conceptual approach for successful integration of nanosensors into unattended smart sensor networks. Biological models such as the organization of social insects or the dynamics of immune systems show promise as biologically-inspired paradigms for protecting nanosensor networks for security scene analysis and battlespace awareness. The paradox of nanosensors is that the smaller the device is the more useful it is but the smaller it is the more vulnerable it is to a variety of threats. In other words simpler means networked nanosensors are more likely to fall prey to a wide-range of attacks including jamming, spoofing, Janisserian recruitment, Pied-Piper distraction, as well as typical attacks computer network security. Thus, unattended sensor technologies call for network architectures that include security and countermeasures to provide reliable scene analysis or battlespace awareness information. Such network centric architectures may well draw upon a variety of bio-inspired approaches to safeguard, validate and make sense of large quantities of information.

Design and Analysis of Self-Adapted Task Scheduling Strategies in Wireless Sensor Networks

PubMed Central

Guo, Wenzhong; Xiong, Naixue; Chao, Han-Chieh; Hussain, Sajid; Chen, Guolong

2011-01-01

In a wireless sensor network (WSN), the usage of resources is usually highly related to the execution of tasks which consume a certain amount of computing and communication bandwidth. Parallel processing among sensors is a promising solution to provide the demanded computation capacity in WSNs. Task allocation and scheduling is a typical problem in the area of high performance computing. Although task allocation and scheduling in wired processor networks has been well studied in the past, their counterparts for WSNs remain largely unexplored. Existing traditional high performance computing solutions cannot be directly implemented in WSNs due to the limitations of WSNs such as limited resource availability and the shared communication medium. In this paper, a self-adapted task scheduling strategy for WSNs is presented. First, a multi-agent-based architecture for WSNs is proposed and a mathematical model of dynamic alliance is constructed for the task allocation problem. Then an effective discrete particle swarm optimization (PSO) algorithm for the dynamic alliance (DPSO-DA) with a well-designed particle position code and fitness function is proposed. A mutation operator which can effectively improve the algorithm’s ability of global search and population diversity is also introduced in this algorithm. Finally, the simulation results show that the proposed solution can achieve significant better performance than other algorithms. PMID:22163971

DOE Office of Scientific and Technical Information (OSTI.GOV)

The Autonomic Intelligent Cyber Sensor (AICS) provides cyber security and industrial network state awareness for Ethernet based control network implementations. The AICS utilizes collaborative mechanisms based on Autonomic Research and a Service Oriented Architecture (SOA) to: 1) identify anomalous network traffic; 2) discover network entity information; 3) deploy deceptive virtual hosts; and 4) implement self-configuring modules. AICS achieves these goals by dynamically reacting to the industrial human-digital ecosystem in which it resides. Information is transported internally and externally on a standards based, flexible two-level communication structure.

An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks

PubMed Central

Mustapha, Ibrahim; Ali, Borhanuddin Mohd; Rasid, Mohd Fadlee A.; Sali, Aduwati; Mohamad, Hafizal

2015-01-01

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach. PMID:26287191

An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks.

PubMed

Mustapha, Ibrahim; Mohd Ali, Borhanuddin; Rasid, Mohd Fadlee A; Sali, Aduwati; Mohamad, Hafizal

2015-08-13

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.

Intelligent Control via Wireless Sensor Networks for Advanced Coal Combustion Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aman Behal; Sunil Kumar; Goodarz Ahmadi

2007-08-05

Numerical Modeling of Solid Gas Flow, System Identification for purposes of modeling and control, and Wireless Sensor and Actor Network design were pursued as part of this project. Time series input-output data was obtained from NETL's Morgantown CFB facility courtesy of Dr. Lawrence Shadle. It was run through a nonlinear kernel estimator and nonparametric models were obtained for the system. Linear and first-order nonlinear kernels were then utilized to obtain a state-space description of the system. Neural networks were trained that performed better at capturing the plant dynamics. It is possible to use these networks to find a plant modelmore » and the inversion of this model can be used to control the system. These models allow one to compare with physics based models whose parameters can then be determined by comparing them against the available data based model. On a parallel track, Dr. Kumar designed an energy-efficient and reliable transport protocol for wireless sensor and actor networks, where the sensors could be different types of wireless sensors used in CFB based coal combustion systems and actors are more powerful wireless nodes to set up a communication network while avoiding the data congestion. Dr. Ahmadi's group studied gas solid flow in a duct. It was seen that particle concentration clearly shows a preferential distribution. The particles strongly interact with the turbulence eddies and are concentrated in narrow bands that are evolving with time. It is believed that observed preferential concentration is due to the fact that these particles are flung out of eddies by centrifugal force.« less

A Middleware Solution for Wireless IoT Applications in Sparse Smart Cities

PubMed Central

Lanzone, Stefano; Riberto, Giulio; Stefanelli, Cesare; Tortonesi, Mauro

2017-01-01

The spread of off-the-shelf mobile devices equipped with multiple wireless interfaces together with sophisticated sensors is paving the way to novel wireless Internet of Things (IoT) environments, characterized by multi-hop infrastructure-less wireless networks where devices carried by users act as sensors/actuators as well as network nodes. In particular, the paper presents Real Ad-hoc Multi-hop Peer-to peer-Wireless IoT Application (RAMP-WIA), a novel solution that facilitates the development, deployment, and management of applications in sparse Smart City environments, characterized by users willing to collaborate by allowing new applications to be deployed on their smartphones to remotely monitor and control fixed/mobile devices. RAMP-WIA allows users to dynamically configure single-hop wireless links, to manage opportunistically multi-hop packet dispatching considering that the network topology (together with the availability of sensors and actuators) may abruptly change, to actuate reliably sensor nodes specifically considering that only part of them could be actually reachable in a timely manner, and to upgrade dynamically the nodes through over-the-air distribution of new software components. The paper also reports the performance of RAMP-WIA on simple but realistic cases of small-scale deployment scenarios with off-the-shelf Android smartphones and Raspberry Pi devices; these results show not only the feasibility and soundness of the proposed approach, but also the efficiency of the middleware implemented when deployed on real testbeds. PMID:29099745

A Middleware Solution for Wireless IoT Applications in Sparse Smart Cities.

PubMed

Bellavista, Paolo; Giannelli, Carlo; Lanzone, Stefano; Riberto, Giulio; Stefanelli, Cesare; Tortonesi, Mauro

2017-11-03

The spread of off-the-shelf mobile devices equipped with multiple wireless interfaces together with sophisticated sensors is paving the way to novel wireless Internet of Things (IoT) environments, characterized by multi-hop infrastructure-less wireless networks where devices carried by users act as sensors/actuators as well as network nodes. In particular, the paper presents Real Ad-hoc Multi-hop Peer-to peer-Wireless IoT Application (RAMP-WIA), a novel solution that facilitates the development, deployment, and management of applications in sparse Smart City environments, characterized by users willing to collaborate by allowing new applications to be deployed on their smartphones to remotely monitor and control fixed/mobile devices. RAMP-WIA allows users to dynamically configure single-hop wireless links, to manage opportunistically multi-hop packet dispatching considering that the network topology (together with the availability of sensors and actuators) may abruptly change, to actuate reliably sensor nodes specifically considering that only part of them could be actually reachable in a timely manner, and to upgrade dynamically the nodes through over-the-air distribution of new software components. The paper also reports the performance of RAMP-WIA on simple but realistic cases of small-scale deployment scenarios with off-the-shelf Android smartphones and Raspberry Pi devices; these results show not only the feasibility and soundness of the proposed approach, but also the efficiency of the middleware implemented when deployed on real testbeds.

Body area network--a key infrastructure element for patient-centered telemedicine.

PubMed

Norgall, Thomas; Schmidt, Robert; von der Grün, Thomas

2004-01-01

The Body Area Network (BAN) extends the range of existing wireless network technologies by an ultra-low range, ultra-low power network solution optimised for long-term or continuous healthcare applications. It enables wireless radio communication between several miniaturised, intelligent Body Sensor (or actor) Units (BSU) and a single Body Central Unit (BCU) worn at the human body. A separate wireless transmission link from the BCU to a network access point--using different technology--provides for online access to BAN components via usual network infrastructure. The BAN network protocol maintains dynamic ad-hoc network configuration scenarios and co-existence of multiple networks.BAN is expected to become a basic infrastructure element for electronic health services: By integrating patient-attached sensors and mobile actor units, distributed information and data processing systems, the range of medical workflow can be extended to include applications like wireless multi-parameter patient monitoring and therapy support. Beyond clinical use and professional disease management environments, private personal health assistance scenarios (without financial reimbursement by health agencies / insurance companies) enable a wide range of applications and services in future pervasive computing and networking environments.

Secure and Efficient Key Coordination Algorithm for Line Topology Network Maintenance for Use in Maritime Wireless Sensor Networks.

PubMed

Elgenaidi, Walid; Newe, Thomas; O'Connell, Eoin; Toal, Daniel; Dooly, Gerard

2016-12-21

There has been a significant increase in the proliferation and implementation of Wireless Sensor Networks (WSNs) in different disciplines, including the monitoring of maritime environments, healthcare systems, and industrial sectors. It has now become critical to address the security issues of data communication while considering sensor node constraints. There are many proposed schemes, including the scheme being proposed in this paper, to ensure that there is a high level of security in WSNs. This paper presents a symmetric security scheme for a maritime coastal environment monitoring WSN. The scheme provides security for travelling packets via individually encrypted links between authenticated neighbors, thus avoiding a reiteration of a global rekeying process. Furthermore, this scheme proposes a dynamic update key based on a trusted node configuration, called a leader node, which works as a trusted third party. The technique has been implemented in real time on a Waspmote test bed sensor platform and the results from both field testing and indoor bench testing environments are discussed in this paper.

Secure and Efficient Key Coordination Algorithm for Line Topology Network Maintenance for Use in Maritime Wireless Sensor Networks

PubMed Central

Elgenaidi, Walid; Newe, Thomas; O’Connell, Eoin; Toal, Daniel; Dooly, Gerard

2016-01-01

There has been a significant increase in the proliferation and implementation of Wireless Sensor Networks (WSNs) in different disciplines, including the monitoring of maritime environments, healthcare systems, and industrial sectors. It has now become critical to address the security issues of data communication while considering sensor node constraints. There are many proposed schemes, including the scheme being proposed in this paper, to ensure that there is a high level of security in WSNs. This paper presents a symmetric security scheme for a maritime coastal environment monitoring WSN. The scheme provides security for travelling packets via individually encrypted links between authenticated neighbors, thus avoiding a reiteration of a global rekeying process. Furthermore, this scheme proposes a dynamic update key based on a trusted node configuration, called a leader node, which works as a trusted third party. The technique has been implemented in real time on a Waspmote test bed sensor platform and the results from both field testing and indoor bench testing environments are discussed in this paper. PMID:28009834

Energy-efficient hierarchical processing in the network of wireless intelligent sensors (WISE)

NASA Astrophysics Data System (ADS)

Raskovic, Dejan

Sensor network nodes have benefited from technological advances in the field of wireless communication, processing, and power sources. However, the processing power of microcontrollers is often not sufficient to perform sophisticated processing, while the power requirements of digital signal processing boards or handheld computers are usually too demanding for prolonged system use. We are matching the intrinsic hierarchical nature of many digital signal-processing applications with the natural hierarchy in distributed wireless networks, and building the hierarchical system of wireless intelligent sensors. Our goal is to build a system that will exploit the hierarchical organization to optimize the power consumption and extend battery life for the given time and memory constraints, while providing real-time processing of sensor signals. In addition, we are designing our system to be able to adapt to the current state of the environment, by dynamically changing the algorithm through procedure replacement. This dissertation presents the analysis of hierarchical environment and methods for energy profiling used to evaluate different system design strategies, and to optimize time-effective and energy-efficient processing.

A BHR Composite Network-Based Visualization Method for Deformation Risk Level of Underground Space

PubMed Central

Zheng, Wei; Zhang, Xiaoya; Lu, Qi

2015-01-01

This study proposes a visualization processing method for the deformation risk level of underground space. The proposed method is based on a BP-Hopfield-RGB (BHR) composite network. Complex environmental factors are integrated in the BP neural network. Dynamic monitoring data are then automatically classified in the Hopfield network. The deformation risk level is combined with the RGB color space model and is displayed visually in real time, after which experiments are conducted with the use of an ultrasonic omnidirectional sensor device for structural deformation monitoring. The proposed method is also compared with some typical methods using a benchmark dataset. Results show that the BHR composite network visualizes the deformation monitoring process in real time and can dynamically indicate dangerous zones. PMID:26011618

Dynamic Routing for Delay-Tolerant Networking in Space Flight Operations

NASA Technical Reports Server (NTRS)

Burleigh, Scott C.

2008-01-01

Contact Graph Routing (CGR) is a dynamic routing system that computes routes through a time-varying topology composed of scheduled, bounded communication contacts in a network built on the Delay-Tolerant Networking (DTN) architecture. It is designed to support operations in a space network based on DTN, but it also could be used in terrestrial applications where operation according to a predefined schedule is preferable to opportunistic communication, as in a low-power sensor network. This paper will describe the operation of the CGR system and explain how it can enable data delivery over scheduled transmission opportunities, fully utilizing the available transmission capacity, without knowing the current state of any bundle protocol node (other than the local node itself) and without exhausting processing resources at any bundle router.

Sensor selection cost optimisation for tracking structurally cyclic systems: a P-order solution

NASA Astrophysics Data System (ADS)

Doostmohammadian, M.; Zarrabi, H.; Rabiee, H. R.

2017-08-01

Measurements and sensing implementations impose certain cost in sensor networks. The sensor selection cost optimisation is the problem of minimising the sensing cost of monitoring a physical (or cyber-physical) system. Consider a given set of sensors tracking states of a dynamical system for estimation purposes. For each sensor assume different costs to measure different (realisable) states. The idea is to assign sensors to measure states such that the global cost is minimised. The number and selection of sensor measurements need to ensure the observability to track the dynamic state of the system with bounded estimation error. The main question we address is how to select the state measurements to minimise the cost while satisfying the observability conditions. Relaxing the observability condition for structurally cyclic systems, the main contribution is to propose a graph theoretic approach to solve the problem in polynomial time. Note that polynomial time algorithms are suitable for large-scale systems as their running time is upper-bounded by a polynomial expression in the size of input for the algorithm. We frame the problem as a linear sum assignment with solution complexity of ?.

Potential of Wake-Up Radio-Based MAC Protocols for Implantable Body Sensor Networks (IBSN)—A Survey

PubMed Central

Karuppiah Ramachandran, Vignesh Raja; Ayele, Eyuel D.; Meratnia, Nirvana; Havinga, Paul J. M.

2016-01-01

With the advent of nano-technology, medical sensors and devices are becoming highly miniaturized. Consequently, the number of sensors and medical devices being implanted to accurately monitor and diagnose a disease is increasing. By measuring the symptoms and controlling a medical device as close as possible to the source, these implantable devices are able to save lives. A wireless link between medical sensors and implantable medical devices is essential in the case of closed-loop medical devices, in which symptoms of the diseases are monitored by sensors that are not placed in close proximity of the therapeutic device. Medium Access Control (MAC) is crucial to make it possible for several medical devices to communicate using a shared wireless medium in such a way that minimum delay, maximum throughput, and increased network life-time are guaranteed. To guarantee this Quality of Service (QoS), the MAC protocols control the main sources of limited resource wastage, namely the idle-listening, packet collisions, over-hearing, and packet loss. Traditional MAC protocols designed for body sensor networks are not directly applicable to Implantable Body Sensor Networks (IBSN) because of the dynamic nature of the radio channel within the human body and the strict QoS requirements of IBSN applications. Although numerous MAC protocols are available in the literature, the majority of them are designed for Body Sensor Network (BSN) and Wireless Sensor Network (WSN). To the best of our knowledge, there is so far no research paper that explores the impact of these MAC protocols specifically for IBSN. MAC protocols designed for implantable devices are still in their infancy and one of their most challenging objectives is to be ultra-low-power. One of the technological solutions to achieve this objective so is to integrate the concept of Wake-up radio (WuR) into the MAC design. In this survey, we present a taxonomy of MAC protocols based on their use of WuR technology and identify their bottlenecks to be used in IBSN applications. Furthermore, we present a number of open research challenges and requirements for designing an energy-efficient and reliable wireless communication protocol for IBSN. PMID:27916822

Potential of Wake-Up Radio-Based MAC Protocols for Implantable Body Sensor Networks (IBSN)-A Survey.

PubMed

Karuppiah Ramachandran, Vignesh Raja; Ayele, Eyuel D; Meratnia, Nirvana; Havinga, Paul J M

2016-11-29

With the advent of nano-technology, medical sensors and devices are becoming highly miniaturized. Consequently, the number of sensors and medical devices being implanted to accurately monitor and diagnose a disease is increasing. By measuring the symptoms and controlling a medical device as close as possible to the source, these implantable devices are able to save lives. A wireless link between medical sensors and implantable medical devices is essential in the case of closed-loop medical devices, in which symptoms of the diseases are monitored by sensors that are not placed in close proximity of the therapeutic device. Medium Access Control (MAC) is crucial to make it possible for several medical devices to communicate using a shared wireless medium in such a way that minimum delay, maximum throughput, and increased network life-time are guaranteed. To guarantee this Quality of Service (QoS), the MAC protocols control the main sources of limited resource wastage, namely the idle-listening, packet collisions, over-hearing, and packet loss. Traditional MAC protocols designed for body sensor networks are not directly applicable to Implantable Body Sensor Networks (IBSN) because of the dynamic nature of the radio channel within the human body and the strict QoS requirements of IBSN applications. Although numerous MAC protocols are available in the literature, the majority of them are designed for Body Sensor Network (BSN) and Wireless Sensor Network (WSN). To the best of our knowledge, there is so far no research paper that explores the impact of these MAC protocols specifically for IBSN. MAC protocols designed for implantable devices are still in their infancy and one of their most challenging objectives is to be ultra-low-power. One of the technological solutions to achieve this objective so is to integrate the concept of Wake-up radio (WuR) into the MAC design. In this survey, we present a taxonomy of MAC protocols based on their use of WuR technology and identify their bottlenecks to be used in IBSN applications. Furthermore, we present a number of open research challenges and requirements for designing an energy-efficient and reliable wireless communication protocol for IBSN.

Improving control and estimation for distributed parameter systems utilizing mobile actuator-sensor network.

PubMed

Mu, Wenying; Cui, Baotong; Li, Wen; Jiang, Zhengxian

2014-07-01

This paper proposes a scheme for non-collocated moving actuating and sensing devices which is unitized for improving performance in distributed parameter systems. By Lyapunov stability theorem, each moving actuator/sensor agent velocity is obtained. To enhance state estimation of a spatially distributes process, two kinds of filters with consensus terms which penalize the disagreement of the estimates are considered. Both filters can result in the well-posedness of the collective dynamics of state errors and can converge to the plant state. Numerical simulations demonstrate that the effectiveness of such a moving actuator-sensor network in enhancing system performance and the consensus filters converge faster to the plant state when consensus terms are included. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

How should social mixing be measured: comparing web-based survey and sensor-based methods.

PubMed

Smieszek, Timo; Barclay, Victoria C; Seeni, Indulaxmi; Rainey, Jeanette J; Gao, Hongjiang; Uzicanin, Amra; Salathé, Marcel

2014-03-10

Contact surveys and diaries have conventionally been used to measure contact networks in different settings for elucidating infectious disease transmission dynamics of respiratory infections. More recently, technological advances have permitted the use of wireless sensor devices, which can be worn by individuals interacting in a particular social context to record high resolution mixing patterns. To date, a direct comparison of these two different methods for collecting contact data has not been performed. We studied the contact network at a United States high school in the spring of 2012. All school members (i.e., students, teachers, and other staff) were invited to wear wireless sensor devices for a single school day, and asked to remember and report the name and duration of all of their close proximity conversational contacts for that day in an online contact survey. We compared the two methods in terms of the resulting network densities, nodal degrees, and degree distributions. We also assessed the correspondence between the methods at the dyadic and individual levels. We found limited congruence in recorded contact data between the online contact survey and wireless sensors. In particular, there was only negligible correlation between the two methods for nodal degree, and the degree distribution differed substantially between both methods. We found that survey underreporting was a significant source of the difference between the two methods, and that this difference could be improved by excluding individuals who reported only a few contact partners. Additionally, survey reporting was more accurate for contacts of longer duration, and very inaccurate for contacts of shorter duration. Finally, female participants tended to report more accurately than male participants. Online contact surveys and wireless sensor devices collected incongruent network data from an identical setting. This finding suggests that these two methods cannot be used interchangeably for informing models of infectious disease dynamics.

A FPGA embedded web server for remote monitoring and control of smart sensors networks.

PubMed

Magdaleno, Eduardo; Rodríguez, Manuel; Pérez, Fernando; Hernández, David; García, Enrique

2013-12-27

This article describes the implementation of a web server using an embedded Altera NIOS II IP core, a general purpose and configurable RISC processor which is embedded in a Cyclone FPGA. The processor uses the μCLinux operating system to support a Boa web server of dynamic pages using Common Gateway Interface (CGI). The FPGA is configured to act like the master node of a network, and also to control and monitor a network of smart sensors or instruments. In order to develop a totally functional system, the FPGA also includes an implementation of the time-triggered protocol (TTP/A). Thus, the implemented master node has two interfaces, the webserver that acts as an Internet interface and the other to control the network. This protocol is widely used to connecting smart sensors and actuators and microsystems in embedded real-time systems in different application domains, e.g., industrial, automotive, domotic, etc., although this protocol can be easily replaced by any other because of the inherent characteristics of the FPGA-based technology.

A FPGA Embedded Web Server for Remote Monitoring and Control of Smart Sensors Networks

PubMed Central

Magdaleno, Eduardo; Rodríguez, Manuel; Pérez, Fernando; Hernández, David; García, Enrique

2014-01-01

This article describes the implementation of a web server using an embedded Altera NIOS II IP core, a general purpose and configurable RISC processor which is embedded in a Cyclone FPGA. The processor uses the μCLinux operating system to support a Boa web server of dynamic pages using Common Gateway Interface (CGI). The FPGA is configured to act like the master node of a network, and also to control and monitor a network of smart sensors or instruments. In order to develop a totally functional system, the FPGA also includes an implementation of the time-triggered protocol (TTP/A). Thus, the implemented master node has two interfaces, the webserver that acts as an Internet interface and the other to control the network. This protocol is widely used to connecting smart sensors and actuators and microsystems in embedded real-time systems in different application domains, e.g., industrial, automotive, domotic, etc., although this protocol can be easily replaced by any other because of the inherent characteristics of the FPGA-based technology. PMID:24379047

Layered Location-Based Security Mechanism for Mobile Sensor Networks: Moving Security Areas.

PubMed

Wang, Ze; Zhang, Haijuan; Wu, Luqiang; Zhou, Chang

2015-09-25

Network security is one of the most important issues in mobile sensor networks (MSNs). Networks are particularly vulnerable in hostile environments because of many factors, such as uncertain mobility, limitations on computation, and the need for storage in mobile nodes. Though some location-based security mechanisms can resist some malicious attacks, they are only suitable for static networks and may sometimes require large amounts of storage. To solve these problems, using location information, which is one of the most important properties in outdoor wireless networks, a security mechanism called a moving security area (MSA) is proposed to resist malicious attacks by using mobile nodes' dynamic location-based keys. The security mechanism is layered by performing different detection schemes inside or outside the MSA. The location-based private keys will be updated only at the appropriate moments, considering the balance of cost and security performance. By transferring parts of the detection tasks from ordinary nodes to the sink node, the memory requirements are distributed to different entities to save limited energy.

GLEON: An Example of Next Generation Network Biogeoscience

NASA Astrophysics Data System (ADS)

Weathers, K. C.; Hanson, P. C.

2014-12-01

When we think of sensor networks, we often focus on hardware development and deployments and the resulting data and synthesis. Yet, for networks that cross institutional boundaries, such as distributed federations of observatories, people are the critical network resource. They establish the linkages and enable access to and interpretation of the data. In the Global Lake Ecological Observatory Network (GLEON), we found that careful integration of three networks --people, hardware, and data--was essential to providing an effective research environment. Accomplishing this integration is not trivial and requires a shared vision among members, explicit attention to the emerging tenets of the science of team science, and training of scientists at all career stages. In GLEON these efforts have resulted in scientific inferences covering new scales, crossing broad ecosystem gradients, and capturing important environmental events. Network-level capital has been increased by the deployment of instrumented buoys, the creation of new data sets and publicly available models, and new ways to synthesize and analyze high frequency data. The formation of international teams of scientists is essential to these goals. Our approach unites a diverse membership in GLEON-style team science, with emphasis on training and engagement of graduate students while creating knowledge. Examples of the bottom-up scientific output from GLEON include creating and confronting models using high frequency data from sensor networks; interpreting output from biological sensors (e.g., algal pigment sensors) as predictors for water quality indices such as water clarity; and understanding the relationship between occasional, highly noxious algal blooms and fluorometric measurements of pigments from sensor networks. Numerical simulation models are not adequate for predicting highly skewed distributions of phytoplankton in eutrophic lakes, suggesting that our fundamental understanding of phytoplankton population dynamics needs modification as do our models, both of which can be improved with the use of high frequency data.

HERA: A New Platform for Embedding Agents in Heterogeneous Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Alonso, Ricardo S.; de Paz, Juan F.; García, Óscar; Gil, Óscar; González, Angélica

Ambient Intelligence (AmI) based systems require the development of innovative solutions that integrate distributed intelligent systems with context-aware technologies. In this sense, Multi-Agent Systems (MAS) and Wireless Sensor Networks (WSN) are two key technologies for developing distributed systems based on AmI scenarios. This paper presents the new HERA (Hardware-Embedded Reactive Agents) platform, that allows using dynamic and self-adaptable heterogeneous WSNs on which agents are directly embedded on the wireless nodes This approach facilitates the inclusion of context-aware capabilities in AmI systems to gather data from their surrounding environments, achieving a higher level of ubiquitous and pervasive computing.

An Efficient Data-Gathering Routing Protocol for Underwater Wireless Sensor Networks.

PubMed

Javaid, Nadeem; Ilyas, Naveed; Ahmad, Ashfaq; Alrajeh, Nabil; Qasim, Umar; Khan, Zahoor Ali; Liaqat, Tayyaba; Khan, Majid Iqbal

2015-11-17

Most applications of underwater wireless sensor networks (UWSNs) demand reliable data delivery over a longer period in an efficient and timely manner. However, the harsh and unpredictable underwater environment makes routing more challenging as compared to terrestrial WSNs. Most of the existing schemes deploy mobile sensors or a mobile sink (MS) to maximize data gathering. However, the relatively high deployment cost prevents their usage in most applications. Thus, this paper presents an autonomous underwater vehicle (AUV)-aided efficient data-gathering (AEDG) routing protocol for reliable data delivery in UWSNs. To prolong the network lifetime, AEDG employs an AUV for data collection from gateways and uses a shortest path tree (SPT) algorithm while associating sensor nodes with the gateways. The AEDG protocol also limits the number of associated nodes with the gateway nodes to minimize the network energy consumption and to prevent the gateways from overloading. Moreover, gateways are rotated with the passage of time to balance the energy consumption of the network. To prevent data loss, AEDG allows dynamic data collection at the AUV depending on the limited number of member nodes that are associated with each gateway. We also develop a sub-optimal elliptical trajectory of AUV by using a connected dominating set (CDS) to further facilitate network throughput maximization. The performance of the AEDG is validated via simulations, which demonstrate the effectiveness of AEDG in comparison to two existing UWSN routing protocols in terms of the selected performance metrics.

An Efficient Data-Gathering Routing Protocol for Underwater Wireless Sensor Networks

PubMed Central

Javaid, Nadeem; Ilyas, Naveed; Ahmad, Ashfaq; Alrajeh, Nabil; Qasim, Umar; Khan, Zahoor Ali; Liaqat, Tayyaba; Khan, Majid Iqbal

2015-01-01

Most applications of underwater wireless sensor networks (UWSNs) demand reliable data delivery over a longer period in an efficient and timely manner. However, the harsh and unpredictable underwater environment makes routing more challenging as compared to terrestrial WSNs. Most of the existing schemes deploy mobile sensors or a mobile sink (MS) to maximize data gathering. However, the relatively high deployment cost prevents their usage in most applications. Thus, this paper presents an autonomous underwater vehicle (AUV)-aided efficient data-gathering (AEDG) routing protocol for reliable data delivery in UWSNs. To prolong the network lifetime, AEDG employs an AUV for data collection from gateways and uses a shortest path tree (SPT) algorithm while associating sensor nodes with the gateways. The AEDG protocol also limits the number of associated nodes with the gateway nodes to minimize the network energy consumption and to prevent the gateways from overloading. Moreover, gateways are rotated with the passage of time to balance the energy consumption of the network. To prevent data loss, AEDG allows dynamic data collection at the AUV depending on the limited number of member nodes that are associated with each gateway. We also develop a sub-optimal elliptical trajectory of AUV by using a connected dominating set (CDS) to further facilitate network throughput maximization. The performance of the AEDG is validated via simulations, which demonstrate the effectiveness of AEDG in comparison to two existing UWSN routing protocols in terms of the selected performance metrics. PMID:26593924

Workflow-Oriented Cyberinfrastructure for Sensor Data Analytics

NASA Astrophysics Data System (ADS)

Orcutt, J. A.; Rajasekar, A.; Moore, R. W.; Vernon, F.

2015-12-01

Sensor streams comprise an increasingly large part of Earth Science data. Analytics based on sensor data require an easy way to perform operations such as acquisition, conversion to physical units, metadata linking, sensor fusion, analysis and visualization on distributed sensor streams. Furthermore, embedding real-time sensor data into scientific workflows is of growing interest. We have implemented a scalable networked architecture that can be used to dynamically access packets of data in a stream from multiple sensors, and perform synthesis and analysis across a distributed network. Our system is based on the integrated Rule Oriented Data System (irods.org), which accesses sensor data from the Antelope Real Time Data System (brtt.com), and provides virtualized access to collections of data streams. We integrate real-time data streaming from different sources, collected for different purposes, on different time and spatial scales, and sensed by different methods. iRODS, noted for its policy-oriented data management, brings to sensor processing features and facilities such as single sign-on, third party access control lists ( ACLs), location transparency, logical resource naming, and server-side modeling capabilities while reducing the burden on sensor network operators. Rich integrated metadata support also makes it straightforward to discover data streams of interest and maintain data provenance. The workflow support in iRODS readily integrates sensor processing into any analytical pipeline. The system is developed as part of the NSF-funded Datanet Federation Consortium (datafed.org). APIs for selecting, opening, reaping and closing sensor streams are provided, along with other helper functions to associate metadata and convert sensor packets into NetCDF and JSON formats. Near real-time sensor data including seismic sensors, environmental sensors, LIDAR and video streams are available through this interface. A system for archiving sensor data and metadata in NetCDF format has been implemented and will be demonstrated at AGU.

Collaborative Catchment-Scale Water Quality Management using Integrated Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Zia, Huma; Harris, Nick; Merrett, Geoff

2013-04-01

Electronics and Computer Science, University of Southampton, United Kingdom Summary The challenge of improving water quality (WQ) is a growing global concern [1]. Poor WQ is mainly attributed to poor water management and outdated agricultural activities. We propose that collaborative sensor networks spread across an entire catchment can allow cooperation among individual activities for integrated WQ monitoring and management. We show that sharing information on critical parameters among networks of water bodies and farms can enable identification and quantification of the contaminant sources, enabling better decision making for agricultural practices and thereby reducing contaminants fluxes. Motivation and results Nutrient losses from land to water have accelerated due to agricultural and urban pursuits [2]. In many cases, the application of fertiliser can be reduced by 30-50% without any loss of yield [3]. Thus information about nutrient levels and trends around the farm can improve agricultural practices and thereby reduce water contamination. The use of sensor networks for monitoring WQ in a catchment is in its infancy, but more applications are being tested [4]. However, these are focussed on local requirements and are mostly limited to water bodies. They have yet to explore the use of this technology for catchment-scale monitoring and management decisions, in an autonomous and dynamic manner. For effective and integrated WQ management, we propose a system that utilises local monitoring networks across a catchment, with provision for collaborative information sharing. This system of networks shares information about critical events, such as rain or flooding. Higher-level applications make use of this information to inform decisions about nutrient management, improving the quality of monitoring through the provision of richer datasets of catchment information to local networks. In the full paper, we present example scenarios and analyse how the benefits of collaborative information sharing can have a direct influence on agricultural practice. We apply a nutrient management scheme to a model of an example catchment with several individual networks. The networks are able to correlate catchment events to events within their zone of influence, allowing them to adapt their monitoring and control strategy in light of wider changes across the catchment. Results indicate that this can lead to significant reductions in nutrient losses (up to 50%) and better reutilization of nutrients amongst farms, having a positive impact on catchment scale water quality and fertilizer costs. 1. EC, E.C., Directive 2000/60/EC establishing a framework for Community action in the field of water policy, 2000. 2. Rivers, M., K. Smettem, and P. Davies. Estimating future scenarios for farm-watershed nutrient fluxes using dynamic simulation modelling-Can on-farm BMPs really do the job at the watershed scale? in Proc.29th Int.Conf System Dynamics Society, 2011. 2010. Washington 3. Liu, C., et al., On-farm evaluation of winter wheat yield response to residual soil nitrate-N in North China Plain. Agronomy Journal, 2008. 100(6): p. 1527-1534. 4. Kotamäki, N., et al., Wireless in-situ sensor network for agriculture and water monitoring on a river basin scale in Southern Finland: Evaluation from a data user's perspective. Sensors, 2009. 9(4): p. 2862-2883.

Underwater Threat Source Localization: Processing Sensor Network TDOAs with a Terascale Optical Core Device

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barhen, Jacob; Imam, Neena

2007-01-01

Revolutionary computing technologies are defined in terms of technological breakthroughs, which leapfrog over near-term projected advances in conventional hardware and software to produce paradigm shifts in computational science. For underwater threat source localization using information provided by a dynamical sensor network, one of the most promising computational advances builds upon the emergence of digital optical-core devices. In this article, we present initial results of sensor network calculations that focus on the concept of signal wavefront time-difference-of-arrival (TDOA). The corresponding algorithms are implemented on the EnLight processing platform recently introduced by Lenslet Laboratories. This tera-scale digital optical core processor is optimizedmore » for array operations, which it performs in a fixed-point-arithmetic architecture. Our results (i) illustrate the ability to reach the required accuracy in the TDOA computation, and (ii) demonstrate that a considerable speed-up can be achieved when using the EnLight 64a prototype processor as compared to a dual Intel XeonTM processor.« less

The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic.

PubMed

Li, Ning; Martínez, José-Fernán; Hernández Díaz, Vicente

2015-08-10

Recently, the cross-layer design for the wireless sensor network communication protocol has become more and more important and popular. Considering the disadvantages of the traditional cross-layer routing algorithms, in this paper we propose a new fuzzy logic-based routing algorithm, named the Balanced Cross-layer Fuzzy Logic (BCFL) routing algorithm. In BCFL, we use the cross-layer parameters' dispersion as the fuzzy logic inference system inputs. Moreover, we give each cross-layer parameter a dynamic weight according the value of the dispersion. For getting a balanced solution, the parameter whose dispersion is large will have small weight, and vice versa. In order to compare it with the traditional cross-layer routing algorithms, BCFL is evaluated through extensive simulations. The simulation results show that the new routing algorithm can handle the multiple constraints without increasing the complexity of the algorithm and can achieve the most balanced performance on selecting the next hop relay node. Moreover, the Balanced Cross-layer Fuzzy Logic routing algorithm can adapt to the dynamic changing of the network conditions and topology effectively.

The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic

PubMed Central

Li, Ning; Martínez, José-Fernán; Díaz, Vicente Hernández

2015-01-01

Recently, the cross-layer design for the wireless sensor network communication protocol has become more and more important and popular. Considering the disadvantages of the traditional cross-layer routing algorithms, in this paper we propose a new fuzzy logic-based routing algorithm, named the Balanced Cross-layer Fuzzy Logic (BCFL) routing algorithm. In BCFL, we use the cross-layer parameters’ dispersion as the fuzzy logic inference system inputs. Moreover, we give each cross-layer parameter a dynamic weight according the value of the dispersion. For getting a balanced solution, the parameter whose dispersion is large will have small weight, and vice versa. In order to compare it with the traditional cross-layer routing algorithms, BCFL is evaluated through extensive simulations. The simulation results show that the new routing algorithm can handle the multiple constraints without increasing the complexity of the algorithm and can achieve the most balanced performance on selecting the next hop relay node. Moreover, the Balanced Cross-layer Fuzzy Logic routing algorithm can adapt to the dynamic changing of the network conditions and topology effectively. PMID:26266412

Secure and lightweight network admission and transmission protocol for body sensor networks.

PubMed

He, Daojing; Chen, Chun; Chan, Sammy; Bu, Jiajun; Zhang, Pingxin

2013-05-01

A body sensor network (BSN) is a wireless network of biosensors and a local processing unit, which is commonly referred to as the personal wireless hub (PWH). Personal health information (PHI) is collected by biosensors and delivered to the PWH before it is forwarded to the remote healthcare center for further processing. In a BSN, it is critical to only admit eligible biosensors and PWH into the network. Also, securing the transmission from each biosensor to PWH is essential not only for ensuring safety of PHI delivery, but also for preserving the privacy of PHI. In this paper, we present the design, implementation, and evaluation of a secure network admission and transmission subsystem based on a polynomial-based authentication scheme. The procedures in this subsystem to establish keys for each biosensor are communication efficient and energy efficient. Moreover, based on the observation that an adversary eavesdropping in a BSN faces inevitable channel errors, we propose to exploit the adversary's uncertainty regarding the PHI transmission to update the individual key dynamically and improve key secrecy. In addition to the theoretical analysis that demonstrates the security properties of our system, this paper also reports the experimental results of the proposed protocol on resource-limited sensor platforms, which show the efficiency of our system in practice.

Mobile Wireless Sensor Networks for Advanced Soil Sensing and Ecosystem Monitoring

NASA Astrophysics Data System (ADS)

Mollenhauer, Hannes; Schima, Robert; Remmler, Paul; Mollenhauer, Olaf; Hutschenreuther, Tino; Toepfer, Hannes; Dietrich, Peter; Bumberger, Jan

2015-04-01

For an adequate characterization of ecosystems it is necessary to detect individual processes with suitable monitoring strategies and methods. Due to the natural complexity of all environmental compartments, single point or temporally and spatially fixed measurements are mostly insufficient for an adequate representation. The application of mobile wireless sensor networks for soil and atmosphere sensing offers significant benefits, due to the simple adjustment of the sensor distribution, the sensor types and the sample rate (e.g. by using optimization approaches or event triggering modes) to the local test conditions. This can be essential for the monitoring of heterogeneous and dynamic environmental systems and processes. One significant advantage in the application of mobile ad-hoc wireless sensor networks is their self-organizing behavior. Thus, the network autonomously initializes and optimizes itself. Due to the localization via satellite a major reduction in installation and operation costs and time is generated. In addition, single point measurements with a sensor are significantly improved by measuring at several optimized points continuously. Since performing analog and digital signal processing and computation in the sensor nodes close to the sensors a significant reduction of the data to be transmitted can be achieved which leads to a better energy management of nodes. Furthermore, the miniaturization of the nodes and energy harvesting are current topics under investigation. First results of field measurements are given to present the potentials and limitations of this application in environmental science. In particular, collected in-situ data with numerous specific soil and atmosphere parameters per sensor node (more than 25) recorded over several days illustrates the high performance of this system for advanced soil sensing and soil-atmosphere interaction monitoring. Moreover, investigations of biotic and abiotic process interactions and optimization of sensor positioning for measuring soil moisture are scopes of this work and initial results of these issues will be presented.

Limitations and tradeoffs in synchronization of large-scale networks with uncertain links

PubMed Central

Diwadkar, Amit; Vaidya, Umesh

2016-01-01

The synchronization of nonlinear systems connected over large-scale networks has gained popularity in a variety of applications, such as power grids, sensor networks, and biology. Stochastic uncertainty in the interconnections is a ubiquitous phenomenon observed in these physical and biological networks. We provide a size-independent network sufficient condition for the synchronization of scalar nonlinear systems with stochastic linear interactions over large-scale networks. This sufficient condition, expressed in terms of nonlinear dynamics, the Laplacian eigenvalues of the nominal interconnections, and the variance and location of the stochastic uncertainty, allows us to define a synchronization margin. We provide an analytical characterization of important trade-offs between the internal nonlinear dynamics, network topology, and uncertainty in synchronization. For nearest neighbour networks, the existence of an optimal number of neighbours with a maximum synchronization margin is demonstrated. An analytical formula for the optimal gain that produces the maximum synchronization margin allows us to compare the synchronization properties of various complex network topologies. PMID:27067994

D-DSC: Decoding Delay-based Distributed Source Coding for Internet of Sensing Things

PubMed Central

Akan, Ozgur B.

2018-01-01

Spatial correlation between densely deployed sensor nodes in a wireless sensor network (WSN) can be exploited to reduce the power consumption through a proper source coding mechanism such as distributed source coding (DSC). In this paper, we propose the Decoding Delay-based Distributed Source Coding (D-DSC) to improve the energy efficiency of the classical DSC by employing the decoding delay concept which enables the use of the maximum correlated portion of sensor samples during the event estimation. In D-DSC, network is partitioned into clusters, where the clusterheads communicate their uncompressed samples carrying the side information, and the cluster members send their compressed samples. Sink performs joint decoding of the compressed and uncompressed samples and then reconstructs the event signal using the decoded sensor readings. Based on the observed degree of the correlation among sensor samples, the sink dynamically updates and broadcasts the varying compression rates back to the sensor nodes. Simulation results for the performance evaluation reveal that D-DSC can achieve reliable and energy-efficient event communication and estimation for practical signal detection/estimation applications having massive number of sensors towards the realization of Internet of Sensing Things (IoST). PMID:29538405

D-DSC: Decoding Delay-based Distributed Source Coding for Internet of Sensing Things.

PubMed

Aktas, Metin; Kuscu, Murat; Dinc, Ergin; Akan, Ozgur B

2018-01-01

Spatial correlation between densely deployed sensor nodes in a wireless sensor network (WSN) can be exploited to reduce the power consumption through a proper source coding mechanism such as distributed source coding (DSC). In this paper, we propose the Decoding Delay-based Distributed Source Coding (D-DSC) to improve the energy efficiency of the classical DSC by employing the decoding delay concept which enables the use of the maximum correlated portion of sensor samples during the event estimation. In D-DSC, network is partitioned into clusters, where the clusterheads communicate their uncompressed samples carrying the side information, and the cluster members send their compressed samples. Sink performs joint decoding of the compressed and uncompressed samples and then reconstructs the event signal using the decoded sensor readings. Based on the observed degree of the correlation among sensor samples, the sink dynamically updates and broadcasts the varying compression rates back to the sensor nodes. Simulation results for the performance evaluation reveal that D-DSC can achieve reliable and energy-efficient event communication and estimation for practical signal detection/estimation applications having massive number of sensors towards the realization of Internet of Sensing Things (IoST).

Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses

PubMed Central

Stephen, Emily P.; Lepage, Kyle Q.; Eden, Uri T.; Brunner, Peter; Schalk, Gerwin; Brumberg, Jonathan S.; Guenther, Frank H.; Kramer, Mark A.

2014-01-01

The brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty—both in the functional network edges and the corresponding aggregate measures of network topology—are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here—appropriate for static and dynamic network inference and different statistical measures of coupling—permits the evaluation of confidence in network measures in a variety of settings common to neuroscience. PMID:24678295

Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses.

PubMed

Stephen, Emily P; Lepage, Kyle Q; Eden, Uri T; Brunner, Peter; Schalk, Gerwin; Brumberg, Jonathan S; Guenther, Frank H; Kramer, Mark A

2014-01-01

The brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty-both in the functional network edges and the corresponding aggregate measures of network topology-are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here-appropriate for static and dynamic network inference and different statistical measures of coupling-permits the evaluation of confidence in network measures in a variety of settings common to neuroscience.

Evaluating reliability of WSN with sleep/wake-up interfering nodes

NASA Astrophysics Data System (ADS)

Distefano, Salvatore

2013-10-01

A wireless sensor network (WSN) (singular and plural of acronyms are spelled the same) is a distributed system composed of autonomous sensor nodes wireless connected and randomly scattered into a geographical area to cooperatively monitor physical or environmental conditions. Adequate techniques and strategies are required to manage a WSN so that it works properly, observing specific quantities and metrics to evaluate the WSN operational conditions. Among them, one of the most important is the reliability. Considering a WSN as a system composed of sensor nodes the system reliability approach can be applied, thus expressing the WSN reliability in terms of its nodes' reliability. More specifically, since often standby power management policies are applied at node level and interferences among nodes may arise, a WSN can be considered as a dynamic system. In this article we therefore consider the WSN reliability evaluation problem from the dynamic system reliability perspective. Static-structural interactions are specified by the WSN topology. Sleep/wake-up standby policies and interferences due to wireless communications can be instead considered as dynamic aspects. Thus, in order to represent and to evaluate the WSN reliability, we use dynamic reliability block diagrams and Petri nets. The proposed technique allows to overcome the limits of Markov models when considering non-linear discharge processes, since they cannot adequately represent the aging processes. In order to demonstrate the effectiveness of the technique, we investigate some specific WSN network topologies, providing guidelines for their representation and evaluation.

Thermal luminescence spectroscopy chemical imaging sensor.

PubMed

Carrieri, Arthur H; Buican, Tudor N; Roese, Erik S; Sutter, James; Samuels, Alan C

2012-10-01

The authors present a pseudo-active chemical imaging sensor model embodying irradiative transient heating, temperature nonequilibrium thermal luminescence spectroscopy, differential hyperspectral imaging, and artificial neural network technologies integrated together. We elaborate on various optimizations, simulations, and animations of the integrated sensor design and apply it to the terrestrial chemical contamination problem, where the interstitial contaminant compounds of detection interest (analytes) comprise liquid chemical warfare agents, their various derivative condensed phase compounds, and other material of a life-threatening nature. The sensor must measure and process a dynamic pattern of absorptive-emissive middle infrared molecular signature spectra of subject analytes to perform its chemical imaging and standoff detection functions successfully.

Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks.

PubMed

Zhou, Zhangbing; Xing, Riliang; Duan, Yucong; Zhu, Yueqin; Xiang, Jianming

2015-12-15

With the advent of the Internet of Underwater Things, smart things are deployed in the ocean space and establish underwater wireless sensor networks for the monitoring of vast and dynamic underwater environments. When events are found to have possibly occurred, accurate event coverage should be detected, and potential event sources should be determined for the enactment of prompt and proper responses. To address this challenge, a technique that detects event coverage and determines event sources is developed in this article. Specifically, the occurrence of possible events corresponds to a set of neighboring sensor nodes whose sensory data may deviate from a normal sensing range in a collective fashion. An appropriate sensor node is selected as the relay node for gathering and routing sensory data to sink node(s). When sensory data are collected at sink node(s), the event coverage is detected and represented as a weighted graph, where the vertices in this graph correspond to sensor nodes and the weight specified upon the edges reflects the extent of sensory data deviating from a normal sensing range. Event sources are determined, which correspond to the barycenters in this graph. The results of the experiments show that our technique is more energy efficient, especially when the network topology is relatively steady.

Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks

PubMed Central

Zhou, Zhangbing; Xing, Riliang; Duan, Yucong; Zhu, Yueqin; Xiang, Jianming

2015-01-01

With the advent of the Internet of Underwater Things, smart things are deployed in the ocean space and establish underwater wireless sensor networks for the monitoring of vast and dynamic underwater environments. When events are found to have possibly occurred, accurate event coverage should be detected, and potential event sources should be determined for the enactment of prompt and proper responses. To address this challenge, a technique that detects event coverage and determines event sources is developed in this article. Specifically, the occurrence of possible events corresponds to a set of neighboring sensor nodes whose sensory data may deviate from a normal sensing range in a collective fashion. An appropriate sensor node is selected as the relay node for gathering and routing sensory data to sink node(s). When sensory data are collected at sink node(s), the event coverage is detected and represented as a weighted graph, where the vertices in this graph correspond to sensor nodes and the weight specified upon the edges reflects the extent of sensory data deviating from a normal sensing range. Event sources are determined, which correspond to the barycenters in this graph. The results of the experiments show that our technique is more energy efficient, especially when the network topology is relatively steady. PMID:26694394

Interacting With A Near Real-Time Urban Digital Watershed Using Emerging Geospatial Web Technologies

NASA Astrophysics Data System (ADS)

Liu, Y.; Fazio, D. J.; Abdelzaher, T.; Minsker, B.

2007-12-01

The value of real-time hydrologic data dissemination including river stage, streamflow, and precipitation for operational stormwater management efforts is particularly high for communities where flash flooding is common and costly. Ideally, such data would be presented within a watershed-scale geospatial context to portray a holistic view of the watershed. Local hydrologic sensor networks usually lack comprehensive integration with sensor networks managed by other agencies sharing the same watershed due to administrative, political, but mostly technical barriers. Recent efforts on providing unified access to hydrological data have concentrated on creating new SOAP-based web services and common data format (e.g. WaterML and Observation Data Model) for users to access the data (e.g. HIS and HydroSeek). Geospatial Web technology including OGC sensor web enablement (SWE), GeoRSS, Geo tags, Geospatial browsers such as Google Earth and Microsoft Virtual Earth and other location-based service tools provides possibilities for us to interact with a digital watershed in near-real-time. OGC SWE proposes a revolutionary concept towards a web-connected/controllable sensor networks. However, these efforts have not provided the capability to allow dynamic data integration/fusion among heterogeneous sources, data filtering and support for workflows or domain specific applications where both push and pull mode of retrieving data may be needed. We propose a light weight integration framework by extending SWE with open source Enterprise Service Bus (e.g., mule) as a backbone component to dynamically transform, transport, and integrate both heterogeneous sensor data sources and simulation model outputs. We will report our progress on building such framework where multi-agencies" sensor data and hydro-model outputs (with map layers) will be integrated and disseminated in a geospatial browser (e.g. Microsoft Virtual Earth). This is a collaborative project among NCSA, USGS Illinois Water Science Center, Computer Science Department at UIUC funded by the Adaptive Environmental Infrastructure Sensing and Information Systems initiative at UIUC.

Identification of Hot Moments and Hot Spots for Real-Time Adaptive Control of Multi-scale Environmental Sensor Networks

NASA Astrophysics Data System (ADS)

Wietsma, T.; Minsker, B. S.

2012-12-01

Increased sensor throughput combined with decreasing hardware costs has led to a disruptive growth in data volume. This disruption, popularly termed "the data deluge," has placed new demands for cyberinfrastructure and information technology skills among researchers in many academic fields, including the environmental sciences. Adaptive sampling has been well established as an effective means of improving network resource efficiency (energy, bandwidth) without sacrificing sample set quality relative to traditional uniform sampling. However, using adaptive sampling for the explicit purpose of improving resolution over events -- situations displaying intermittent dynamics and unique hydrogeological signatures -- is relatively new. In this paper, we define hot spots and hot moments in terms of sensor signal activity as measured through discrete Fourier analysis. Following this frequency-based approach, we apply the Nyquist-Shannon sampling theorem, a fundamental contribution from signal processing that led to the field of information theory, for analysis of uni- and multivariate environmental signal data. In the scope of multi-scale environmental sensor networks, we present several sampling control algorithms, derived from the Nyquist-Shannon theorem, that operate at local (field sensor), regional (base station for aggregation of field sensor data), and global (Cloud-based, computationally intensive models) scales. Evaluated over soil moisture data, results indicate significantly greater sample density during precipitation events while reducing overall sample volume. Using these algorithms as indicators rather than control mechanisms, we also discuss opportunities for spatio-temporal modeling as a tool for planning/modifying sensor network deployments. Locally adaptive model based on Nyquist-Shannon sampling theorem Pareto frontiers for local, regional, and global models relative to uniform sampling. Objectives are (1) overall sampling efficiency and (2) sampling efficiency during hot moments as identified using heuristic approach.

Game theoretic sensor management for target tracking

NASA Astrophysics Data System (ADS)

Shen, Dan; Chen, Genshe; Blasch, Erik; Pham, Khanh; Douville, Philip; Yang, Chun; Kadar, Ivan

2010-04-01

This paper develops and evaluates a game-theoretic approach to distributed sensor-network management for target tracking via sensor-based negotiation. We present a distributed sensor-based negotiation game model for sensor management for multi-sensor multi-target tacking situations. In our negotiation framework, each negotiation agent represents a sensor and each sensor maximizes their utility using a game approach. The greediness of each sensor is limited by the fact that the sensor-to-target assignment efficiency will decrease if too many sensor resources are assigned to a same target. It is similar to the market concept in real world, such as agreements between buyers and sellers in an auction market. Sensors are willing to switch targets so that they can obtain their highest utility and the most efficient way of applying their resources. Our sub-game perfect equilibrium-based negotiation strategies dynamically and distributedly assign sensors to targets. Numerical simulations are performed to demonstrate our sensor-based negotiation approach for distributed sensor management.

Autonomic Intelligent Cyber Sensor to Support Industrial Control Network Awareness

DOE PAGES

Vollmer, Todd; Manic, Milos; Linda, Ondrej

2013-06-01

The proliferation of digital devices in a networked industrial ecosystem, along with an exponential growth in complexity and scope, has resulted in elevated security concerns and management complexity issues. This paper describes a novel architecture utilizing concepts of Autonomic computing and a SOAP based IF-MAP external communication layer to create a network security sensor. This approach simplifies integration of legacy software and supports a secure, scalable, self-managed framework. The contribution of this paper is two-fold: 1) A flexible two level communication layer based on Autonomic computing and Service Oriented Architecture is detailed and 2) Three complementary modules that dynamically reconfiguremore » in response to a changing environment are presented. One module utilizes clustering and fuzzy logic to monitor traffic for abnormal behavior. Another module passively monitors network traffic and deploys deceptive virtual network hosts. These components of the sensor system were implemented in C++ and PERL and utilize a common internal D-Bus communication mechanism. A proof of concept prototype was deployed on a mixed-use test network showing the possible real world applicability. In testing, 45 of the 46 network attached devices were recognized and 10 of the 12 emulated devices were created with specific Operating System and port configurations. Additionally the anomaly detection algorithm achieved a 99.9% recognition rate. All output from the modules were correctly distributed using the common communication structure.« less

A Game-Theoretic Response Strategy for Coordinator Attack in Wireless Sensor Networks

PubMed Central

Liu, Jianhua; Yue, Guangxue; Shang, Huiliang; Li, Hongjie

2014-01-01

The coordinator is a specific node that controls the whole network and has a significant impact on the performance in cooperative multihop ZigBee wireless sensor networks (ZWSNs). However, the malicious node attacks coordinator nodes in an effort to waste the resources and disrupt the operation of the network. Attacking leads to a failure of one round of communication between the source nodes and destination nodes. Coordinator selection is a technique that can considerably defend against attack and reduce the data delivery delay, and increase network performance of cooperative communications. In this paper, we propose an adaptive coordinator selection algorithm using game and fuzzy logic aiming at both minimizing the average number of hops and maximizing network lifetime. The proposed game model consists of two interrelated formulations: a stochastic game for dynamic defense and a best response policy using evolutionary game formulation for coordinator selection. The stable equilibrium best policy to response defense is obtained from this game model. It is shown that the proposed scheme can improve reliability and save energy during the network lifetime with respect to security. PMID:25105171

A game-theoretic response strategy for coordinator attack in wireless sensor networks.

PubMed

Liu, Jianhua; Yue, Guangxue; Shen, Shigen; Shang, Huiliang; Li, Hongjie

2014-01-01

The coordinator is a specific node that controls the whole network and has a significant impact on the performance in cooperative multihop ZigBee wireless sensor networks (ZWSNs). However, the malicious node attacks coordinator nodes in an effort to waste the resources and disrupt the operation of the network. Attacking leads to a failure of one round of communication between the source nodes and destination nodes. Coordinator selection is a technique that can considerably defend against attack and reduce the data delivery delay, and increase network performance of cooperative communications. In this paper, we propose an adaptive coordinator selection algorithm using game and fuzzy logic aiming at both minimizing the average number of hops and maximizing network lifetime. The proposed game model consists of two interrelated formulations: a stochastic game for dynamic defense and a best response policy using evolutionary game formulation for coordinator selection. The stable equilibrium best policy to response defense is obtained from this game model. It is shown that the proposed scheme can improve reliability and save energy during the network lifetime with respect to security.

Monitoring Bloom Dynamics of a Common Coastal Bioluminescent Ctenophore

DTIC Science & Technology

2010-09-30

photodiodes. IMPACT/APPLICATIONS More frequent and more rapidly developing jellyfish blooms, especially Mnemiopsis leidyi as well as Harmful Algal...To meet the need for a bioluminescent jellyfish monitoring and forecasting system, predictive models will depend upon dense networks of sensor

Adaptively Adjusted Event-Triggering Mechanism on Fault Detection for Networked Control Systems.

PubMed

Wang, Yu-Long; Lim, Cheng-Chew; Shi, Peng

2016-12-08

This paper studies the problem of adaptively adjusted event-triggering mechanism-based fault detection for a class of discrete-time networked control system (NCS) with applications to aircraft dynamics. By taking into account the fault occurrence detection progress and the fault occurrence probability, and introducing an adaptively adjusted event-triggering parameter, a novel event-triggering mechanism is proposed to achieve the efficient utilization of the communication network bandwidth. Both the sensor-to-control station and the control station-to-actuator network-induced delays are taken into account. The event-triggered sensor and the event-triggered control station are utilized simultaneously to establish new network-based closed-loop models for the NCS subject to faults. Based on the established models, the event-triggered simultaneous design of fault detection filter (FDF) and controller is presented. A new algorithm for handling the adaptively adjusted event-triggering parameter is proposed. Performance analysis verifies the effectiveness of the adaptively adjusted event-triggering mechanism, and the simultaneous design of FDF and controller.

A General Self-Organized Tree-Based Energy-Balance Routing Protocol for Wireless Sensor Network

NASA Astrophysics Data System (ADS)

Han, Zhao; Wu, Jie; Zhang, Jie; Liu, Liefeng; Tian, Kaiyun

2014-04-01

Wireless sensor network (WSN) is a system composed of a large number of low-cost micro-sensors. This network is used to collect and send various kinds of messages to a base station (BS). WSN consists of low-cost nodes with limited battery power, and the battery replacement is not easy for WSN with thousands of physically embedded nodes, which means energy efficient routing protocol should be employed to offer a long-life work time. To achieve the aim, we need not only to minimize total energy consumption but also to balance WSN load. Researchers have proposed many protocols such as LEACH, HEED, PEGASIS, TBC and PEDAP. In this paper, we propose a General Self-Organized Tree-Based Energy-Balance routing protocol (GSTEB) which builds a routing tree using a process where, for each round, BS assigns a root node and broadcasts this selection to all sensor nodes. Subsequently, each node selects its parent by considering only itself and its neighbors' information, thus making GSTEB a dynamic protocol. Simulation results show that GSTEB has a better performance than other protocols in balancing energy consumption, thus prolonging the lifetime of WSN.

Elliptic Curve Cryptography with Security System in Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Huang, Xu; Sharma, Dharmendra

2010-10-01

The rapid progress of wireless communications and embedded micro-electro-system technologies has made wireless sensor networks (WSN) very popular and even become part of our daily life. WSNs design are generally application driven, namely a particular application's requirements will determine how the network behaves. However, the natures of WSN have attracted increasing attention in recent years due to its linear scalability, a small software footprint, low hardware implementation cost, low bandwidth requirement, and high device performance. It is noted that today's software applications are mainly characterized by their component-based structures which are usually heterogeneous and distributed, including the WSNs. But WSNs typically need to configure themselves automatically and support as hoc routing. Agent technology provides a method for handling increasing software complexity and supporting rapid and accurate decision making. This paper based on our previous works [1, 2], three contributions have made, namely (a) fuzzy controller for dynamic slide window size to improve the performance of running ECC (b) first presented a hidden generation point for protection from man-in-the middle attack and (c) we first investigates multi-agent applying for key exchange together. Security systems have been drawing great attentions as cryptographic algorithms have gained popularity due to the natures that make them suitable for use in constrained environment such as mobile sensor information applications, where computing resources and power availability are limited. Elliptic curve cryptography (ECC) is one of high potential candidates for WSNs, which requires less computational power, communication bandwidth, and memory in comparison with other cryptosystem. For saving pre-computing storages recently there is a trend for the sensor networks that the sensor group leaders rather than sensors communicate to the end database, which highlighted the needs to prevent from the man-in-the middle attack. A designed a hidden generator point that offer a good protection from the man-in-the middle (MinM) attack which becomes one of major worries for the sensor's networks with multiagent system is also discussed.

Assimilating uncertain, dynamic and intermittent streamflow observations in hydrological models

NASA Astrophysics Data System (ADS)

Mazzoleni, Maurizio; Alfonso, Leonardo; Chacon-Hurtado, Juan; Solomatine, Dimitri

2015-09-01

Catastrophic floods cause significant socio-economical losses. Non-structural measures, such as real-time flood forecasting, can potentially reduce flood risk. To this end, data assimilation methods have been used to improve flood forecasts by integrating static ground observations, and in some cases also remote sensing observations, within water models. Current hydrologic and hydraulic research works consider assimilation of observations coming from traditional, static sensors. At the same time, low-cost, mobile sensors and mobile communication devices are becoming also increasingly available. The main goal and innovation of this study is to demonstrate the usefulness of assimilating uncertain streamflow observations that are dynamic in space and intermittent in time in the context of two different semi-distributed hydrological model structures. The developed method is applied to the Brue basin, where the dynamic observations are imitated by the synthetic observations of discharge. The results of this study show how model structures and sensors locations affect in different ways the assimilation of streamflow observations. In addition, it proves how assimilation of such uncertain observations from dynamic sensors can provide model improvements similar to those of streamflow observations coming from a non-optimal network of static physical sensors. This can be a potential application of recent efforts to build citizen observatories of water, which can make the citizens an active part in information capturing, evaluation and communication, helping simultaneously to improvement of model-based flood forecasting.

Robust Structural Analysis and Design of Distributed Control Systems to Prevent Zero Dynamics Attacks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weerakkody, Sean; Liu, Xiaofei; Sinopoli, Bruno

We consider the design and analysis of robust distributed control systems (DCSs) to ensure the detection of integrity attacks. DCSs are often managed by independent agents and are implemented using a diverse set of sensors and controllers. However, the heterogeneous nature of DCSs along with their scale leave such systems vulnerable to adversarial behavior. To mitigate this reality, we provide tools that allow operators to prevent zero dynamics attacks when as many as p agents and sensors are corrupted. Such a design ensures attack detectability in deterministic systems while removing the threat of a class of stealthy attacks in stochasticmore » systems. To achieve this goal, we use graph theory to obtain necessary and sufficient conditions for the presence of zero dynamics attacks in terms of the structural interactions between agents and sensors. We then formulate and solve optimization problems which minimize communication networks while also ensuring a resource limited adversary cannot perform a zero dynamics attacks. Polynomial time algorithms for design and analysis are provided.« less

Monitoring of Carbon Dioxide and Methane Plumes from Combined Ground-Airborne Sensors

NASA Astrophysics Data System (ADS)

Jacob, Jamey; Mitchell, Taylor; Honeycutt, Wes; Materer, Nicholas; Ley, Tyler; Clark, Peter

2016-11-01

A hybrid ground-airborne sensing network for real-time plume monitoring of CO2 and CH4 for carbon sequestration is investigated. Conventional soil gas monitoring has difficulty in distinguishing gas flux signals from leakage with those associated with meteorologically driven changes. A low-cost, lightweight sensor system has been developed and implemented onboard a small unmanned aircraft and is combined with a large-scale ground network that measures gas concentration. These are combined with other atmospheric diagnostics, including thermodynamic data and velocity from ultrasonic anemometers and multi-hole probes. To characterize the system behavior and verify its effectiveness, field tests have been conducted with simulated discharges of CO2 and CH4 from compressed gas tanks to mimic leaks and generate gaseous plumes, as well as field tests over the Farnsworth CO2-EOR site in the Anadarko Basin. Since the sensor response time is a function of vehicle airspeed, dynamic calibration models are required to determine accurate location of gas concentration in space and time. Comparisons are made between the two tests and results compared with historical models combining both flight and atmospheric dynamics. Supported by Department of Energy Award DE-FE0012173.

Feedforward Categorization on AER Motion Events Using Cortex-Like Features in a Spiking Neural Network.

PubMed

Zhao, Bo; Ding, Ruoxi; Chen, Shoushun; Linares-Barranco, Bernabe; Tang, Huajin

2015-09-01

This paper introduces an event-driven feedforward categorization system, which takes data from a temporal contrast address event representation (AER) sensor. The proposed system extracts bio-inspired cortex-like features and discriminates different patterns using an AER based tempotron classifier (a network of leaky integrate-and-fire spiking neurons). One of the system's most appealing characteristics is its event-driven processing, with both input and features taking the form of address events (spikes). The system was evaluated on an AER posture dataset and compared with two recently developed bio-inspired models. Experimental results have shown that it consumes much less simulation time while still maintaining comparable performance. In addition, experiments on the Mixed National Institute of Standards and Technology (MNIST) image dataset have demonstrated that the proposed system can work not only on raw AER data but also on images (with a preprocessing step to convert images into AER events) and that it can maintain competitive accuracy even when noise is added. The system was further evaluated on the MNIST dynamic vision sensor dataset (in which data is recorded using an AER dynamic vision sensor), with testing accuracy of 88.14%.

Flexible Fusion Structure-Based Performance Optimization Learning for Multisensor Target Tracking

PubMed Central

Ge, Quanbo; Wei, Zhongliang; Cheng, Tianfa; Chen, Shaodong; Wang, Xiangfeng

2017-01-01

Compared with the fixed fusion structure, the flexible fusion structure with mixed fusion methods has better adjustment performance for the complex air task network systems, and it can effectively help the system to achieve the goal under the given constraints. Because of the time-varying situation of the task network system induced by moving nodes and non-cooperative target, and limitations such as communication bandwidth and measurement distance, it is necessary to dynamically adjust the system fusion structure including sensors and fusion methods in a given adjustment period. Aiming at this, this paper studies the design of a flexible fusion algorithm by using an optimization learning technology. The purpose is to dynamically determine the sensors’ numbers and the associated sensors to take part in the centralized and distributed fusion processes, respectively, herein termed sensor subsets selection. Firstly, two system performance indexes are introduced. Especially, the survivability index is presented and defined. Secondly, based on the two indexes and considering other conditions such as communication bandwidth and measurement distance, optimization models for both single target tracking and multi-target tracking are established. Correspondingly, solution steps are given for the two optimization models in detail. Simulation examples are demonstrated to validate the proposed algorithms. PMID:28481243

Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV.

PubMed

Abbaspour, Alireza; Aboutalebi, Payam; Yen, Kang K; Sargolzaei, Arman

2017-03-01

A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Differential Privacy Preserving in Big Data Analytics for Connected Health.

PubMed

Lin, Chi; Song, Zihao; Song, Houbing; Zhou, Yanhong; Wang, Yi; Wu, Guowei

2016-04-01

In Body Area Networks (BANs), big data collected by wearable sensors usually contain sensitive information, which is compulsory to be appropriately protected. Previous methods neglected privacy protection issue, leading to privacy exposure. In this paper, a differential privacy protection scheme for big data in body sensor network is developed. Compared with previous methods, this scheme will provide privacy protection with higher availability and reliability. We introduce the concept of dynamic noise thresholds, which makes our scheme more suitable to process big data. Experimental results demonstrate that, even when the attacker has full background knowledge, the proposed scheme can still provide enough interference to big sensitive data so as to preserve the privacy.

On the MAC/network/energy performance evaluation of Wireless Sensor Networks: Contrasting MPH, AODV, DSR and ZTR routing protocols.

PubMed

Del-Valle-Soto, Carolina; Mex-Perera, Carlos; Orozco-Lugo, Aldo; Lara, Mauricio; Galván-Tejada, Giselle M; Olmedo, Oscar

2014-12-02

Wireless Sensor Networks deliver valuable information for long periods, then it is desirable to have optimum performance, reduced delays, low overhead, and reliable delivery of information. In this work, proposed metrics that influence energy consumption are used for a performance comparison among our proposed routing protocol, called Multi-Parent Hierarchical (MPH), the well-known protocols for sensor networks, Ad hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), and Zigbee Tree Routing (ZTR), all of them working with the IEEE 802.15.4 MAC layer. Results show how some communication metrics affect performance, throughput, reliability and energy consumption. It can be concluded that MPH is an efficient protocol since it reaches the best performance against the other three protocols under evaluation, such as 19.3% reduction of packet retransmissions, 26.9% decrease of overhead, and 41.2% improvement on the capacity of the protocol for recovering the topology from failures with respect to AODV protocol. We implemented and tested MPH in a real network of 99 nodes during ten days and analyzed parameters as number of hops, connectivity and delay, in order to validate our Sensors 2014, 14 22812 simulator and obtain reliable results. Moreover, an energy model of CC2530 chip is proposed and used for simulations of the four aforementioned protocols, showing that MPH has 15.9% reduction of energy consumption with respect to AODV, 13.7% versus DSR, and 5% against ZTR.

Augmenting Trust Establishment in Dynamic Systems with Social Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lagesse, Brent J; Kumar, Mohan; Venkatesh, Svetha

2010-01-01

Social networking has recently flourished in popularity through the use of social websites. Pervasive computing resources have allowed people stay well-connected to each other through access to social networking resources. We take the position that utilizing information produced by relationships within social networks can assist in the establishment of trust for other pervasive computing applications. Furthermore, we describe how such a system can augment a sensor infrastructure used for event observation with information from mobile sensors (ie, mobile phones with cameras) controlled by potentially untrusted third parties. Pervasive computing systems are invisible systems, oriented around the user. As a result,more » many future pervasive systems are likely to include a social aspect to the system. The social communities that are developed in these systems can augment existing trust mechanisms with information about pre-trusted entities or entities to initially consider when beginning to establish trust. An example of such a system is the Collaborative Virtual Observation (CoVO) system fuses sensor information from disaparate sources in soft real-time to recreate a scene that provides observation of an event that has recently transpired. To accomplish this, CoVO must efficently access services whilst protecting the data from corruption from unknown remote nodes. CoVO combines dynamic service composition with virtual observation to utilize existing infrastructure with third party services available in the environment. Since these services are not under the control of the system, they may be unreliable or malicious. When an event of interest occurs, the given infrastructure (bus cameras, etc.) may not sufficiently cover the necessary information (be it in space, time, or sensor type). To enhance observation of the event, infrastructure is augmented with information from sensors in the environment that the infrastructure does not control. These sensors may be unreliable, uncooperative, or even malicious. Additionally, to execute queries in soft real-time, processing must be distributed to available systems in the environment. We propose to use information from social networks to satisfy these requirements. In this paper, we present our position that knowledge gained from social activities can be used to augment trust mechanisms in pervasive computing. The system uses social behavior of nodes to predict a subset that it wants to query for information. In this context, social behavior such as transit patterns and schedules (which can be used to determine if a queried node is likely to be reliable) or known relationships, such as a phone's address book, that can be used to determine networks of nodes that may also be able to assist in retrieving information. Neither implicit nor explicit relationships necessarily imply that the user trusts an entity, but rather will provide a starting place for establishing trust. The proposed framework utilizes social network information to assist in trust establishment when third-party sensors are used for sensing events.« less

VORBrouter: A dynamic data routing system for Real-Time Seismic networks

NASA Astrophysics Data System (ADS)

Hansen, T.; Vernon, F.; Lindquist, K.; Orcutt, J.

2004-12-01

For anyone who has managed a moderately complex buffered real-time data transport system, the need for reliable adaptive data transport is clear. The ROADNet VORBrouter system, an extension to the ROADNet data catalog system [AGU-2003, Dynamic Dataflow Topology Monitoring for Real-time Seismic Networks], allows dynamic routing of real-time seismic data from sensor to end-user. Traditional networks consist of a series of data buffer computers with data transport interconnections configured by hand. This allows for arbitrarily complex data networks, which can often exceed full comprehension by network administrators, sometimes resulting in data loops or accidental data cutoff. In order to manage data transport systems in the event of a network failure, a network administrator must be called upon to change the data transport paths and to recover the missing data. Using VORBrouter, administrators can sleep at night while still providing 7/24 uninterupted data streams at realistic cost. This software package uses information from the ROADNet data catalog system to route packets around failed link outages and to new consumers in real-time. Dynamic data routing protocols operating on top of the Antelope Data buffering layer allow authorized users to request data sets from their local buffer and to have them delivered from anywhere within the network of buffers. The VORBrouter software also allows for dynamic routing around network outages, and the elimination of duplicate data paths within the network, while maintaining the nearly lossless data transport features exhibited by the underlying Antelope system. We present the design of the VORBrouter system, its features, limitations and some future research directions.

Sensor Network Architectures for Monitoring Underwater Pipelines

PubMed Central

Mohamed, Nader; Jawhar, Imad; Al-Jaroodi, Jameela; Zhang, Liren

2011-01-01

This paper develops and compares different sensor network architecture designs that can be used for monitoring underwater pipeline infrastructures. These architectures are underwater wired sensor networks, underwater acoustic wireless sensor networks, RF (Radio Frequency) wireless sensor networks, integrated wired/acoustic wireless sensor networks, and integrated wired/RF wireless sensor networks. The paper also discusses the reliability challenges and enhancement approaches for these network architectures. The reliability evaluation, characteristics, advantages, and disadvantages among these architectures are discussed and compared. Three reliability factors are used for the discussion and comparison: the network connectivity, the continuity of power supply for the network, and the physical network security. In addition, the paper also develops and evaluates a hierarchical sensor network framework for underwater pipeline monitoring. PMID:22346669

Sensor network architectures for monitoring underwater pipelines.

PubMed

Mohamed, Nader; Jawhar, Imad; Al-Jaroodi, Jameela; Zhang, Liren

2011-01-01

This paper develops and compares different sensor network architecture designs that can be used for monitoring underwater pipeline infrastructures. These architectures are underwater wired sensor networks, underwater acoustic wireless sensor networks, RF (radio frequency) wireless sensor networks, integrated wired/acoustic wireless sensor networks, and integrated wired/RF wireless sensor networks. The paper also discusses the reliability challenges and enhancement approaches for these network architectures. The reliability evaluation, characteristics, advantages, and disadvantages among these architectures are discussed and compared. Three reliability factors are used for the discussion and comparison: the network connectivity, the continuity of power supply for the network, and the physical network security. In addition, the paper also develops and evaluates a hierarchical sensor network framework for underwater pipeline monitoring.

The Strategic Direction for Army Science and Technology

DTIC Science & Technology

2013-02-01

methods to characterize the nature of trust (e.g., trust in information, trust in a network node or link), and to take measures to manage the trust...Science and Technology Executive, Dr. Thomas Killion, requested a study of peer review methods in use at Army laboratories. The paper discusses... sensors  Characterization of network dynamics and quality of information important to tactical decision-making Work that should be supported

Use of Energy Efficient Sensor Networks to Enhance Dynamic Data Gathering Systems: A Comparative Study between Bluetooth and ZigBee.

PubMed

Gheorghiu, Razvan Andrei; Iordache, Valentin

2018-06-03

As road traffic conditions worsen due to the constantly increasing number of cars, traffic management systems are struggling to provide a suitable environment, by gathering all the relevant information from the road network. However, in most cases these are obtained via traffic detectors placed near road junctions, thus providing no information on the conditions in between. A large-scale sensor network using detectors on the majority of vehicles would certainly be capable of providing useful data, but has two major impediments: the equipment installed on the vehicles should be cheap enough (assuming the willingness of private car owners to be a part of the network) and be capable of transferring the required amount of data in due time, as the vehicle passes by the road side unit that acts as interface with the traffic management system. These restrictions reduce the number of technologies that can be used. In this article a series of comprehensive tests have been performed to evaluate the Bluetooth and ZigBee protocols for this purpose from many points of view: handshake time, static and dynamic data transfer (in laboratory conditions and in real traffic conditions). An assessment of the environmental conditions (during tests and probable to be encountered in real conditions) was also provided.

Agent Collaborative Target Localization and Classification in Wireless Sensor Networks

PubMed Central

Wang, Xue; Bi, Dao-wei; Ding, Liang; Wang, Sheng

2007-01-01

Wireless sensor networks (WSNs) are autonomous networks that have been frequently deployed to collaboratively perform target localization and classification tasks. Their autonomous and collaborative features resemble the characteristics of agents. Such similarities inspire the development of heterogeneous agent architecture for WSN in this paper. The proposed agent architecture views WSN as multi-agent systems and mobile agents are employed to reduce in-network communication. According to the architecture, an energy based acoustic localization algorithm is proposed. In localization, estimate of target location is obtained by steepest descent search. The search algorithm adapts to measurement environments by dynamically adjusting its termination condition. With the agent architecture, target classification is accomplished by distributed support vector machine (SVM). Mobile agents are employed for feature extraction and distributed SVM learning to reduce communication load. Desirable learning performance is guaranteed by combining support vectors and convex hull vectors. Fusion algorithms are designed to merge SVM classification decisions made from various modalities. Real world experiments with MICAz sensor nodes are conducted for vehicle localization and classification. Experimental results show the proposed agent architecture remarkably facilitates WSN designs and algorithm implementation. The localization and classification algorithms also prove to be accurate and energy efficient.

Development of esMOCA Biomechanic, Motion Capture Instrumentation for Biomechanics Analysis

NASA Astrophysics Data System (ADS)

Arendra, A.; Akhmad, S.

2018-01-01

This study aims to build motion capture instruments using inertial measurement unit sensors to assist in the analysis of biomechanics. Sensors used are accelerometer and gyroscope. Estimation of orientation sensors is done by digital motion processing in each sensor nodes. There are nine sensor nodes attached to the upper limbs. This sensor is connected to the pc via a wireless sensor network. The development of kinematics and inverse dynamamic models of the upper limb is done in simulink simmechanic. The kinematic model receives streaming data of sensor nodes mounted on the limbs. The output of the kinematic model is the pose of each limbs and visualized on display. The dynamic inverse model outputs the reaction force and reaction moment of each joint based on the limb motion input. Model validation in simulink with mathematical model of mechanical analysis showed results that did not differ significantly

Emergency navigation without an infrastructure.

PubMed

Gelenbe, Erol; Bi, Huibo

2014-08-18

Emergency navigation systems for buildings and other built environments, such as sport arenas or shopping centres, typically rely on simple sensor networks to detect emergencies and, then, provide automatic signs to direct the evacuees. The major drawbacks of such static wireless sensor network (WSN)-based emergency navigation systems are the very limited computing capacity, which makes adaptivity very difficult, and the restricted battery power, due to the low cost of sensor nodes for unattended operation. If static wireless sensor networks and cloud-computing can be integrated, then intensive computations that are needed to determine optimal evacuation routes in the presence of time-varying hazards can be offloaded to the cloud, but the disadvantages of limited battery life-time at the client side, as well as the high likelihood of system malfunction during an emergency still remain. By making use of the powerful sensing ability of smart phones, which are increasingly ubiquitous, this paper presents a cloud-enabled indoor emergency navigation framework to direct evacuees in a coordinated fashion and to improve the reliability and resilience for both communication and localization. By combining social potential fields (SPF) and a cognitive packet network (CPN)-based algorithm, evacuees are guided to exits in dynamic loose clusters. Rather than relying on a conventional telecommunications infrastructure, we suggest an ad hoc cognitive packet network (AHCPN)-based protocol to adaptively search optimal communication routes between portable devices and the network egress nodes that provide access to cloud servers, in a manner that spares the remaining battery power of smart phones and minimizes the time latency. Experimental results through detailed simulations indicate that smart human motion and smart network management can increase the survival rate of evacuees and reduce the number of drained smart phones in an evacuation process.

Emergency Navigation without an Infrastructure

PubMed Central

Gelenbe, Erol; Bi, Huibo

2014-01-01

Emergency navigation systems for buildings and other built environments, such as sport arenas or shopping centres, typically rely on simple sensor networks to detect emergencies and, then, provide automatic signs to direct the evacuees. The major drawbacks of such static wireless sensor network (WSN)-based emergency navigation systems are the very limited computing capacity, which makes adaptivity very difficult, and the restricted battery power, due to the low cost of sensor nodes for unattended operation. If static wireless sensor networks and cloud-computing can be integrated, then intensive computations that are needed to determine optimal evacuation routes in the presence of time-varying hazards can be offloaded to the cloud, but the disadvantages of limited battery life-time at the client side, as well as the high likelihood of system malfunction during an emergency still remain. By making use of the powerful sensing ability of smart phones, which are increasingly ubiquitous, this paper presents a cloud-enabled indoor emergency navigation framework to direct evacuees in a coordinated fashion and to improve the reliability and resilience for both communication and localization. By combining social potential fields (SPF) and a cognitive packet network (CPN)-based algorithm, evacuees are guided to exits in dynamic loose clusters. Rather than relying on a conventional telecommunications infrastructure, we suggest an ad hoc cognitive packet network (AHCPN)-based protocol to adaptively search optimal communication routes between portable devices and the network egress nodes that provide access to cloud servers, in a manner that spares the remaining battery power of smart phones and minimizes the time latency. Experimental results through detailed simulations indicate that smart human motion and smart network management can increase the survival rate of evacuees and reduce the number of drained smart phones in an evacuation process. PMID:25196014

Impact of reduced scale free network on wireless sensor network

NASA Astrophysics Data System (ADS)

Keshri, Neha; Gupta, Anurag; Mishra, Bimal Kumar

2016-12-01

In heterogeneous wireless sensor network (WSN) each data-packet traverses through multiple hops over restricted communication range before it reaches the sink. The amount of energy required to transmit a data-packet is directly proportional to the number of hops. To balance the energy costs across the entire network and to enhance the robustness in order to improve the lifetime of WSN becomes a key issue of researchers. Due to high dimensionality of an epidemic model of WSN over a general scale free network, it is quite difficult to have close study of network dynamics. To overcome this complexity, we simplify a general scale free network by partitioning all of its motes into two classes: higher-degree motes and lower-degree motes, and equating the degrees of all higher-degree motes with lower-degree motes, yielding a reduced scale free network. We develop an epidemic model of WSN based on reduced scale free network. The existence of unique positive equilibrium is determined with some restrictions. Stability of the system is proved. Furthermore, simulation results show improvements made in this paper have made the entire network have a better robustness to the network failure and the balanced energy costs. This reduced model based on scale free network theory proves more applicable to the research of WSN.

Simulation of Greenhouse Climate Monitoring and Control with Wireless Sensor Network and Event-Based Control

PubMed Central

Pawlowski, Andrzej; Guzman, Jose Luis; Rodríguez, Francisco; Berenguel, Manuel; Sánchez, José; Dormido, Sebastián

2009-01-01

Monitoring and control of the greenhouse environment play a decisive role in greenhouse production processes. Assurance of optimal climate conditions has a direct influence on crop growth performance, but it usually increases the required equipment cost. Traditionally, greenhouse installations have required a great effort to connect and distribute all the sensors and data acquisition systems. These installations need many data and power wires to be distributed along the greenhouses, making the system complex and expensive. For this reason, and others such as unavailability of distributed actuators, only individual sensors are usually located in a fixed point that is selected as representative of the overall greenhouse dynamics. On the other hand, the actuation system in greenhouses is usually composed by mechanical devices controlled by relays, being desirable to reduce the number of commutations of the control signals from security and economical point of views. Therefore, and in order to face these drawbacks, this paper describes how the greenhouse climate control can be represented as an event-based system in combination with wireless sensor networks, where low-frequency dynamics variables have to be controlled and control actions are mainly calculated against events produced by external disturbances. The proposed control system allows saving costs related with wear minimization and prolonging the actuator life, but keeping promising performance results. Analysis and conclusions are given by means of simulation results. PMID:22389597

Simulation of greenhouse climate monitoring and control with wireless sensor network and event-based control.

PubMed

Pawlowski, Andrzej; Guzman, Jose Luis; Rodríguez, Francisco; Berenguel, Manuel; Sánchez, José; Dormido, Sebastián

2009-01-01

Monitoring and control of the greenhouse environment play a decisive role in greenhouse production processes. Assurance of optimal climate conditions has a direct influence on crop growth performance, but it usually increases the required equipment cost. Traditionally, greenhouse installations have required a great effort to connect and distribute all the sensors and data acquisition systems. These installations need many data and power wires to be distributed along the greenhouses, making the system complex and expensive. For this reason, and others such as unavailability of distributed actuators, only individual sensors are usually located in a fixed point that is selected as representative of the overall greenhouse dynamics. On the other hand, the actuation system in greenhouses is usually composed by mechanical devices controlled by relays, being desirable to reduce the number of commutations of the control signals from security and economical point of views. Therefore, and in order to face these drawbacks, this paper describes how the greenhouse climate control can be represented as an event-based system in combination with wireless sensor networks, where low-frequency dynamics variables have to be controlled and control actions are mainly calculated against events produced by external disturbances. The proposed control system allows saving costs related with wear minimization and prolonging the actuator life, but keeping promising performance results. Analysis and conclusions are given by means of simulation results.

An Effective Hybrid Routing Algorithm in WSN: Ant Colony Optimization in combination with Hop Count Minimization.

PubMed

Jiang, Ailian; Zheng, Lihong

2018-03-29

Low cost, high reliability and easy maintenance are key criteria in the design of routing protocols for wireless sensor networks (WSNs). This paper investigates the existing ant colony optimization (ACO)-based WSN routing algorithms and the minimum hop count WSN routing algorithms by reviewing their strengths and weaknesses. We also consider the critical factors of WSNs, such as energy constraint of sensor nodes, network load balancing and dynamic network topology. Then we propose a hybrid routing algorithm that integrates ACO and a minimum hop count scheme. The proposed algorithm is able to find the optimal routing path with minimal total energy consumption and balanced energy consumption on each node. The algorithm has unique superiority in terms of searching for the optimal path, balancing the network load and the network topology maintenance. The WSN model and the proposed algorithm have been implemented using C++. Extensive simulation experimental results have shown that our algorithm outperforms several other WSN routing algorithms on such aspects that include the rate of convergence, the success rate in searching for global optimal solution, and the network lifetime.

An Effective Hybrid Routing Algorithm in WSN: Ant Colony Optimization in combination with Hop Count Minimization

PubMed Central

2018-01-01

Low cost, high reliability and easy maintenance are key criteria in the design of routing protocols for wireless sensor networks (WSNs). This paper investigates the existing ant colony optimization (ACO)-based WSN routing algorithms and the minimum hop count WSN routing algorithms by reviewing their strengths and weaknesses. We also consider the critical factors of WSNs, such as energy constraint of sensor nodes, network load balancing and dynamic network topology. Then we propose a hybrid routing algorithm that integrates ACO and a minimum hop count scheme. The proposed algorithm is able to find the optimal routing path with minimal total energy consumption and balanced energy consumption on each node. The algorithm has unique superiority in terms of searching for the optimal path, balancing the network load and the network topology maintenance. The WSN model and the proposed algorithm have been implemented using C++. Extensive simulation experimental results have shown that our algorithm outperforms several other WSN routing algorithms on such aspects that include the rate of convergence, the success rate in searching for global optimal solution, and the network lifetime. PMID:29596336

Reconfigurable routing protocol for free space optical sensor networks.

PubMed

Xie, Rong; Yang, Won-Hyuk; Kim, Young-Chon

2012-01-01

Recently, free space optical sensor networks (FSOSNs), which are based on free space optics (FSO) instead of radio frequency (RF), have gained increasing visibility over traditional wireless sensor networks (WSNs) due to their advantages such as larger capacity, higher security, and lower cost. However, the performance of FSOSNs is restricted to the requirement of a direct line-of-sight (LOS) path between a sender and a receiver pair. Once a node dies of energy depletion, the network would probably suffer from a dramatic decrease of connectivity, resulting in a huge loss of data packets. Thus, this paper proposes a reconfigurable routing protocol (RRP) to overcome this problem by dynamically reconfiguring the network virtual topology. The RRP works in three phases: (1) virtual topology construction, (2) routing establishment, and (3) reconfigurable routing. When data transmission begins, the data packets are first routed through the shortest hop paths. Then a reconfiguration is initiated by the node whose residual energy falls below a threshold. Nodes affected by this dying node are classified into two types, namely maintenance nodes and adjustment nodes, and they are reconfigured according to the types. An energy model is designed to evaluate the performance of RRP through OPNET simulation. Our simulation results indicate that the RRP achieves better performance compared with the simple-link protocol and a direct reconfiguration scheme in terms of connectivity, network lifetime, packet delivery ratio and the number of living nodes.

An Experimental Study on Static and Dynamic Strain Sensitivity of Embeddable Smart Concrete Sensors Doped with Carbon Nanotubes for SHM of Large Structures.

PubMed

Meoni, Andrea; D'Alessandro, Antonella; Downey, Austin; García-Macías, Enrique; Rallini, Marco; Materazzi, A Luigi; Torre, Luigi; Laflamme, Simon; Castro-Triguero, Rafael; Ubertini, Filippo

2018-03-09

The availability of new self-sensing cement-based strain sensors allows the development of dense sensor networks for Structural Health Monitoring (SHM) of reinforced concrete structures. These sensors are fabricated by doping cement-matrix mterials with conductive fillers, such as Multi Walled Carbon Nanotubes (MWCNTs), and can be embedded into structural elements made of reinforced concrete prior to casting. The strain sensing principle is based on the multifunctional composites outputting a measurable change in their electrical properties when subjected to a deformation. Previous work by the authors was devoted to material fabrication, modeling and applications in SHM. In this paper, we investigate the behavior of several sensors fabricated with and without aggregates and with different MWCNT contents. The strain sensitivity of the sensors, in terms of fractional change in electrical resistivity for unit strain, as well as their linearity are investigated through experimental testing under both quasi-static and sine-sweep dynamic uni-axial compressive loadings. Moreover, the responses of the sensors when subjected to destructive compressive tests are evaluated. Overall, the presented results contribute to improving the scientific knowledge on the behavior of smart concrete sensors and to furthering their understanding for SHM applications.

An Experimental Study on Static and Dynamic Strain Sensitivity of Embeddable Smart Concrete Sensors Doped with Carbon Nanotubes for SHM of Large Structures

PubMed Central

Meoni, Andrea; D’Alessandro, Antonella; García-Macías, Enrique; Rallini, Marco; Materazzi, A. Luigi; Torre, Luigi; Laflamme, Simon; Castro-Triguero, Rafael

2018-01-01

The availability of new self-sensing cement-based strain sensors allows the development of dense sensor networks for Structural Health Monitoring (SHM) of reinforced concrete structures. These sensors are fabricated by doping cement-matrix mterials with conductive fillers, such as Multi Walled Carbon Nanotubes (MWCNTs), and can be embedded into structural elements made of reinforced concrete prior to casting. The strain sensing principle is based on the multifunctional composites outputting a measurable change in their electrical properties when subjected to a deformation. Previous work by the authors was devoted to material fabrication, modeling and applications in SHM. In this paper, we investigate the behavior of several sensors fabricated with and without aggregates and with different MWCNT contents. The strain sensitivity of the sensors, in terms of fractional change in electrical resistivity for unit strain, as well as their linearity are investigated through experimental testing under both quasi-static and sine-sweep dynamic uni-axial compressive loadings. Moreover, the responses of the sensors when subjected to destructive compressive tests are evaluated. Overall, the presented results contribute to improving the scientific knowledge on the behavior of smart concrete sensors and to furthering their understanding for SHM applications. PMID:29522498

Fiber optic sensor for monitoring a density of road traffic

NASA Astrophysics Data System (ADS)

Nedoma, Jan; Fajkus, Marcel; Martinek, Radek; Mec, Pavel; Novak, Martin; Jargus, Jan; Vasinek, Vladimir

2017-10-01

Authors of this article have focused on the use of fiber-optic technology in the car traffic. The article describes the use of fiber-optic interferometer for the purpose of the dynamic calculation of traffic density and inclusion the vehicle into the traffic lane. The objective is to increase safety and traffic flow. Presented solution is characterized by the non-destructive character to the road - sensor no need built into the roadway. The sensor works with standard telecommunications fibers of the G.652 standard. Other hallmarks are immunity to electromagnetic interference (EMI) and passivity of concerning the power supply. The massive expansion of optical cables within telecommunication needs along roads offers the possibility of connecting to the existing telecommunications fiber-optic network without a converter. Information can be transmitted at distances of several km up to tens km by this fiber-optic network. Set of experimental measurements in real traffic flow verified the functionality of presented solution.

Energy efficient wireless sensor networks by using a fuzzy-based solution

NASA Astrophysics Data System (ADS)

Tirrito, Salvatore; Nicolosi, Giuseppina

2016-12-01

Wireless Sensor Networks are characterized by a distributed architecture realized by a set of autonomous electronic devices able to sense data from the surrounding environment and to communicate among them. These devices are battery powered since they may be used even to monitor hazardous events in inaccessible areas. As a consequence, it is preferable to assure the adoption of energy management solutions in order to extend the WSN lifetime, as far as possible. Moreover, it is crucial to guarantee that the nodes receive the transmitted data correctly. It is clear that trading off power optimization and quality of service has become one the most important concerns when dealing with modern systems based on WSNs. This paper introduces a solution based on a Fuzzy Logic Controller (FLC) focusing on the minimization of energy consumption of wireless sensor nodes. This is made possible because the sleeping time of these nodes is dynamically regulated by a FLC.

Markov logic network based complex event detection under uncertainty

NASA Astrophysics Data System (ADS)

Lu, Jingyang; Jia, Bin; Chen, Genshe; Chen, Hua-mei; Sullivan, Nichole; Pham, Khanh; Blasch, Erik

2018-05-01

In a cognitive reasoning system, the four-stage Observe-Orient-Decision-Act (OODA) reasoning loop is of interest. The OODA loop is essential for the situational awareness especially in heterogeneous data fusion. Cognitive reasoning for making decisions can take advantage of different formats of information such as symbolic observations, various real-world sensor readings, or the relationship between intelligent modalities. Markov Logic Network (MLN) provides mathematically sound technique in presenting and fusing data at multiple levels of abstraction, and across multiple intelligent sensors to conduct complex decision-making tasks. In this paper, a scenario about vehicle interaction is investigated, in which uncertainty is taken into consideration as no systematic approaches can perfectly characterize the complex event scenario. MLNs are applied to the terrestrial domain where the dynamic features and relationships among vehicles are captured through multiple sensors and information sources regarding the data uncertainty.

Chaotic itinerancy within the coupled dynamics between a physical body and neural oscillator networks.

PubMed

Park, Jihoon; Mori, Hiroki; Okuyama, Yuji; Asada, Minoru

2017-01-01

Chaotic itinerancy is a phenomenon in which the state of a nonlinear dynamical system spontaneously explores and attracts certain states in a state space. From this perspective, the diverse behavior of animals and its spontaneous transitions lead to a complex coupled dynamical system, including a physical body and a brain. Herein, a series of simulations using different types of non-linear oscillator networks (i.e., regular, small-world, scale-free, random) with a musculoskeletal model (i.e., a snake-like robot) as a physical body are conducted to understand how the chaotic itinerancy of bodily behavior emerges from the coupled dynamics between the body and the brain. A behavior analysis (behavior clustering) and network analysis for the classified behavior are then applied. The former consists of feature vector extraction from the motions and classification of the movement patterns that emerged from the coupled dynamics. The network structures behind the classified movement patterns are revealed by estimating the "information networks" different from the given non-linear oscillator networks based on the transfer entropy which finds the information flow among neurons. The experimental results show that: (1) the number of movement patterns and their duration depend on the sensor ratio to control the balance of strength between the body and the brain dynamics and on the type of the given non-linear oscillator networks; and (2) two kinds of information networks are found behind two kinds movement patterns with different durations by utilizing the complex network measures, clustering coefficient and the shortest path length with a negative and a positive relationship with the duration periods of movement patterns. The current results seem promising for a future extension of the method to a more complicated body and environment. Several requirements are also discussed.

A WBAN System for Ambulatory Monitoring of Physical Activity and Health Status: Applications and Challenges.

PubMed

Jovanov, E; Milenkovic, A; Otto, C; De Groen, P; Johnson, B; Warren, S; Taibi, G

2005-01-01

Recent technological advances in sensors, low-power integrated circuits, and wireless communications have enabled the design of low-cost, miniature, lightweight, intelligent physiological sensor platforms that can be seamlessly integrated into a body area network for health monitoring. Wireless body area networks (WBANs) promise unobtrusive ambulatory health monitoring for extended periods of time and near real-time updates of patients' medical records through the Internet. A number of innovative systems for health monitoring have recently been proposed. However, they typically rely on custom communication protocols and hardware designs, lacking generality and flexibility. The lack of standard platforms, system software support, and standards makes these systems expensive. Bulky sensors, high price, and frequent battery changes are all likely to limit user compliance. To address some of these challenges, we prototyped a WBAN utilizing a common off-the-shelf wireless sensor platform with a ZigBee-compliant radio interface and an ultra low-power microcontroller. The standard platform interfaces to custom sensor boards that are equipped with accelerometers for motion monitoring and a bioamplifier for electrocardiogram or electromyogram monitoring. Software modules for on-board processing, communication, and network synchronization have been developed using the TinyOS operating system. Although the initial WBAN prototype targets ambulatory monitoring of user activity, the developed sensors can easily be adapted to monitor other physiological parameters. In this paper, we discuss initial results, implementation challenges, and the need for standardization in this dynamic and promising research field.

Sensor Authentication in Collaborating Sensor Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bielefeldt, Jake Uriah

2014-11-01

In this thesis, we address a new security problem in the realm of collaborating sensor networks. By collaborating sensor networks, we refer to the networks of sensor networks collaborating on a mission, with each sensor network is independently owned and operated by separate entities. Such networks are practical where a number of independent entities can deploy their own sensor networks in multi-national, commercial, and environmental scenarios, and some of these networks will integrate complementary functionalities for a mission. In the scenario, we address an authentication problem wherein the goal is for the Operator O i of Sensor Network S imore » to correctly determine the number of active sensors in Network Si. Such a problem is challenging in collaborating sensor networks where other sensor networks, despite showing an intent to collaborate, may not be completely trustworthy and could compromise the authentication process. We propose two authentication protocols to address this problem. Our protocols rely on Physically Unclonable Functions, which are a hardware based authentication primitive exploiting inherent randomness in circuit fabrication. Our protocols are light-weight, energy efficient, and highly secure against a number of attacks. To the best of our knowledge, ours is the first to addresses a practical security problem in collaborating sensor networks.« less

An efficient management system for wireless sensor networks.

PubMed

Ma, Yi-Wei; Chen, Jiann-Liang; Huang, Yueh-Min; Lee, Mei-Yu

2010-01-01

Wireless sensor networks have garnered considerable attention recently. Networks typically have many sensor nodes, and are used in commercial, medical, scientific, and military applications for sensing and monitoring the physical world. Many researchers have attempted to improve wireless sensor network management efficiency. A Simple Network Management Protocol (SNMP)-based sensor network management system was developed that is a convenient and effective way for managers to monitor and control sensor network operations. This paper proposes a novel WSNManagement system that can show the connections stated of relationships among sensor nodes and can be used for monitoring, collecting, and analyzing information obtained by wireless sensor networks. The proposed network management system uses collected information for system configuration. The function of performance analysis facilitates convenient management of sensors. Experimental results show that the proposed method enhances the alive rate of an overall sensor node system, reduces the packet lost rate by roughly 5%, and reduces delay time by roughly 0.2 seconds. Performance analysis demonstrates that the proposed system is effective for wireless sensor network management.

Neural Network Substorm Identification: Enabling TREx Sensor Web Modes

NASA Astrophysics Data System (ADS)

Chaddock, D.; Spanswick, E.; Arnason, K. M.; Donovan, E.; Liang, J.; Ahmad, S.; Jackel, B. J.

2017-12-01

Transition Region Explorer (TREx) is a ground-based sensor web of optical and radio instruments that is presently being deployed across central Canada. The project consists of an array of co-located blue-line, full-colour, and near-infrared all-sky imagers, imaging riometers, proton aurora spectrographs, and GNSS systems. A key goal of the TREx project is to create the world's first (artificial) intelligent sensor web for remote sensing space weather. The sensor web will autonomously control and coordinate instrument operations in real-time. To accomplish this, we will use real-time in-line analytics of TREx and other data to dynamically switch between operational modes. An operating mode could be, for example, to have a blue-line imager gather data at a one or two orders of magnitude higher cadence than it operates for its `baseline' mode. The software decision to increase the imaging cadence would be in response to an anticipated increase in auroral activity or other programmatic requirements. Our first test for TREx's sensor web technologies is to develop the capacity to autonomously alter the TREx operating mode prior to a substorm expansion phase onset. In this paper, we present our neural network analysis of historical optical and riometer data and our ability to predict an optical onset. We explore the preliminary insights into using a neural network to pick out trends and features which it deems are similar among substorms.

Dynamic and diverse sugar signaling

PubMed Central

Li, Lei; Sheen, Jen

2016-01-01

Sugars fuel life and exert numerous regulatory actions that are fundamental to all life forms. There are two principal mechanisms underlie sugar “perception and signal transduction” in biological systems. Direct sensing and signaling is triggered via sugar-binding sensors with a broad range of affinity and specificity, whereas sugar-derived bioenergetic molecules and metabolites modulate signaling proteins and indirectly relay sugar signals. This review discusses the emerging sugar signals and potential sugar sensors discovered in plant systems. The findings leading to informative understanding of physiological regulation by sugars are considered and assessed. Comparative transcriptome analyses highlight the primary and dynamic sugar responses and reveal the convergent and specific regulators of key biological processes in the sugar-signaling network. PMID:27423125

Three-Factor User Authentication and Key Agreement Using Elliptic Curve Cryptosystem in Wireless Sensor Networks.

PubMed

Park, YoHan; Park, YoungHo

2016-12-14

Secure communication is a significant issue in wireless sensor networks. User authentication and key agreement are essential for providing a secure system, especially in user-oriented mobile services. It is also necessary to protect the identity of each individual in wireless environments to avoid personal privacy concerns. Many authentication and key agreement schemes utilize a smart card in addition to a password to support security functionalities. However, these schemes often fail to provide security along with privacy. In 2015, Chang et al. analyzed the security vulnerabilities of previous schemes and presented the two-factor authentication scheme that provided user privacy by using dynamic identities. However, when we cryptanalyzed Chang et al.'s scheme, we found that it does not provide sufficient security for wireless sensor networks and fails to provide accurate password updates. This paper proposes a security-enhanced authentication and key agreement scheme to overcome these security weaknesses using biometric information and an elliptic curve cryptosystem. We analyze the security of the proposed scheme against various attacks and check its viability in the mobile environment.

A Novel Physical Layer Assisted Authentication Scheme for Mobile Wireless Sensor Networks

PubMed Central

Wang, Qiuhua

2017-01-01

Physical-layer authentication can address physical layer vulnerabilities and security threats in wireless sensor networks, and has been considered as an effective complementary enhancement to existing upper-layer authentication mechanisms. In this paper, to advance the existing research and improve the authentication performance, we propose a novel physical layer assisted authentication scheme for mobile wireless sensor networks. In our proposed scheme, we explore the reciprocity and spatial uncorrelation of the wireless channel to verify the identities of involved transmitting users and decide whether all data frames are from the same sender. In our proposed scheme, a new method is developed for the legitimate users to compare their received signal strength (RSS) records, which avoids the information from being disclosed to the adversary. Our proposed scheme can detect the spoofing attack even in a high dynamic environment. We evaluate our scheme through experiments under indoor and outdoor environments. Experiment results show that our proposed scheme is more efficient and achieves a higher detection rate as well as keeping a lower false alarm rate. PMID:28165423

Three-Factor User Authentication and Key Agreement Using Elliptic Curve Cryptosystem in Wireless Sensor Networks

PubMed Central

Park, YoHan; Park, YoungHo

2016-01-01

Secure communication is a significant issue in wireless sensor networks. User authentication and key agreement are essential for providing a secure system, especially in user-oriented mobile services. It is also necessary to protect the identity of each individual in wireless environments to avoid personal privacy concerns. Many authentication and key agreement schemes utilize a smart card in addition to a password to support security functionalities. However, these schemes often fail to provide security along with privacy. In 2015, Chang et al. analyzed the security vulnerabilities of previous schemes and presented the two-factor authentication scheme that provided user privacy by using dynamic identities. However, when we cryptanalyzed Chang et al.’s scheme, we found that it does not provide sufficient security for wireless sensor networks and fails to provide accurate password updates. This paper proposes a security-enhanced authentication and key agreement scheme to overcome these security weaknesses using biometric information and an elliptic curve cryptosystem. We analyze the security of the proposed scheme against various attacks and check its viability in the mobile environment. PMID:27983616

A Novel Physical Layer Assisted Authentication Scheme for Mobile Wireless Sensor Networks.

PubMed

Wang, Qiuhua

2017-02-04

Physical-layer authentication can address physical layer vulnerabilities and security threats in wireless sensor networks, and has been considered as an effective complementary enhancement to existing upper-layer authentication mechanisms. In this paper, to advance the existing research and improve the authentication performance, we propose a novel physical layer assisted authentication scheme for mobile wireless sensor networks. In our proposed scheme, we explore the reciprocity and spatial uncorrelation of the wireless channel to verify the identities of involved transmitting users and decide whether all data frames are from the same sender. In our proposed scheme, a new method is developed for the legitimate users to compare their received signal strength (RSS) records, which avoids the information from being disclosed to the adversary. Our proposed scheme can detect the spoofing attack even in a high dynamic environment. We evaluate our scheme through experiments under indoor and outdoor environments. Experiment results show that our proposed scheme is more efficient and achieves a higher detection rate as well as keeping a lower false alarm rate.

Adaptive limited feedback for interference alignment in MIMO interference channels.

PubMed

Zhang, Yang; Zhao, Chenglin; Meng, Juan; Li, Shibao; Li, Li

2016-01-01

It is very important that the radar sensor network has autonomous capabilities such as self-managing, etc. Quite often, MIMO interference channels are applied to radar sensor networks, and for self-managing purpose, interference management in MIMO interference channels is critical. Interference alignment (IA) has the potential to dramatically improve system throughput by effectively mitigating interference in multi-user networks at high signal-to-noise (SNR). However, the implementation of IA predominantly relays on perfect and global channel state information (CSI) at all transceivers. A large amount of CSI has to be fed back to all transmitters, resulting in a proliferation of feedback bits. Thus, IA with limited feedback has been introduced to reduce the sum feedback overhead. In this paper, by exploiting the advantage of heterogeneous path loss, we first investigate the throughput of IA with limited feedback in interference channels while each user transmits multi-streams simultaneously, then we get the upper bound of sum rate in terms of the transmit power and feedback bits. Moreover, we propose a dynamic feedback scheme via bit allocation to reduce the throughput loss due to limited feedback. Simulation results demonstrate that the dynamic feedback scheme achieves better performance in terms of sum rate.

Effects of Seasonal Land Surface Conditions on Hydrometeorological Dynamics in South-western North America

DTIC Science & Technology

2015-09-21

vehicles, environmental sensor networks, distributed hydrologic modeling, vegetation dynamics, soil moisture, evapotranspiration , remote sensing, North...Received Paper 1.00 5.00 3.00 8.00 9.00 E. Vivoni, J. Rodriguez, C. Watts. On the spatiotemporal variability of soil moisture and evapotranspiration ...Vegetation Impacts on Evapotranspiration and Its Partitioning at the Catchment Scale during SMEX04–NAME, Journal of Hydrometeorology, (10 2012

Efficient data communication protocols for wireless networks

NASA Astrophysics Data System (ADS)

Zeydan, Engin

In this dissertation, efficient decentralized algorithms are investigated for cost minimization problems in wireless networks. For wireless sensor networks, we investigate both the reduction in the energy consumption and throughput maximization problems separately using multi-hop data aggregation for correlated data in wireless sensor networks. The proposed algorithms exploit data redundancy using a game theoretic framework. For energy minimization, routes are chosen to minimize the total energy expended by the network using best response dynamics to local data. The cost function used in routing takes into account distance, interference and in-network data aggregation. The proposed energy-efficient correlation-aware routing algorithm significantly reduces the energy consumption in the network and converges in a finite number of steps iteratively. For throughput maximization, we consider both the interference distribution across the network and correlation between forwarded data when establishing routes. Nodes along each route are chosen to minimize the interference impact in their neighborhood and to maximize the in-network data aggregation. The resulting network topology maximizes the global network throughput and the algorithm is guaranteed to converge with a finite number of steps using best response dynamics. For multiple antenna wireless ad-hoc networks, we present distributed cooperative and regret-matching based learning schemes for joint transmit beanformer and power level selection problem for nodes operating in multi-user interference environment. Total network transmit power is minimized while ensuring a constant received signal-to-interference and noise ratio at each receiver. In cooperative and regret-matching based power minimization algorithms, transmit beanformers are selected from a predefined codebook to minimize the total power. By selecting transmit beamformers judiciously and performing power adaptation, the cooperative algorithm is shown to converge to pure strategy Nash equilibrium with high probability throughout the iterations in the interference impaired network. On the other hand, the regret-matching learning algorithm is noncooperative and requires minimum amount of overhead. The proposed cooperative and regret-matching based distributed algorithms are also compared with centralized solutions through simulation results.

NPS Collaborative Technology Testbed for ONR CKM Program

DTIC Science & Technology

2005-01-11

or have access to the MIT E-Wall hosted by the TOC. The combination of E-Wall and agents lend themselves to the dynamic gathering and display of...display, intuitive icons or menus that is easy to activate and customize , and automatically seeks and connects to other like services/networks/agents...integration creates network- centric memory mechanism for developing shared understanding of SA events Data Base Integration of Sensor-DM Agents and

Hybrid architecture for building secure sensor networks

NASA Astrophysics Data System (ADS)

Owens, Ken R., Jr.; Watkins, Steve E.

2012-04-01

Sensor networks have various communication and security architectural concerns. Three approaches are defined to address these concerns for sensor networks. The first area is the utilization of new computing architectures that leverage embedded virtualization software on the sensor. Deploying a small, embedded virtualization operating system on the sensor nodes that is designed to communicate to low-cost cloud computing infrastructure in the network is the foundation to delivering low-cost, secure sensor networks. The second area focuses on securing the sensor. Sensor security components include developing an identification scheme, and leveraging authentication algorithms and protocols that address security assurance within the physical, communication network, and application layers. This function will primarily be accomplished through encrypting the communication channel and integrating sensor network firewall and intrusion detection/prevention components to the sensor network architecture. Hence, sensor networks will be able to maintain high levels of security. The third area addresses the real-time and high priority nature of the data that sensor networks collect. This function requires that a quality-of-service (QoS) definition and algorithm be developed for delivering the right data at the right time. A hybrid architecture is proposed that combines software and hardware features to handle network traffic with diverse QoS requirements.

Multi-channel multi-radio using 802.11 based media access for sink nodes in wireless sensor networks.

PubMed

Campbell, Carlene E-A; Khan, Shafiullah; Singh, Dhananjay; Loo, Kok-Keong

2011-01-01

The next generation surveillance and multimedia systems will become increasingly deployed as wireless sensor networks in order to monitor parks, public places and for business usage. The convergence of data and telecommunication over IP-based networks has paved the way for wireless networks. Functions are becoming more intertwined by the compelling force of innovation and technology. For example, many closed-circuit TV premises surveillance systems now rely on transmitting their images and data over IP networks instead of standalone video circuits. These systems will increase their reliability in the future on wireless networks and on IEEE 802.11 networks. However, due to limited non-overlapping channels, delay, and congestion there will be problems at sink nodes. In this paper we provide necessary conditions to verify the feasibility of round robin technique in these networks at the sink nodes by using a technique to regulate multi-radio multichannel assignment. We demonstrate through simulations that dynamic channel assignment scheme using multi-radio, and multichannel configuration at a single sink node can perform close to optimal on the average while multiple sink node assignment also performs well. The methods proposed in this paper can be a valuable tool for network designers in planning network deployment and for optimizing different performance objectives.

Multi-Channel Multi-Radio Using 802.11 Based Media Access for Sink Nodes in Wireless Sensor Networks

PubMed Central

Campbell, Carlene E.-A.; Khan, Shafiullah; Singh, Dhananjay; Loo, Kok-Keong

2011-01-01

The next generation surveillance and multimedia systems will become increasingly deployed as wireless sensor networks in order to monitor parks, public places and for business usage. The convergence of data and telecommunication over IP-based networks has paved the way for wireless networks. Functions are becoming more intertwined by the compelling force of innovation and technology. For example, many closed-circuit TV premises surveillance systems now rely on transmitting their images and data over IP networks instead of standalone video circuits. These systems will increase their reliability in the future on wireless networks and on IEEE 802.11 networks. However, due to limited non-overlapping channels, delay, and congestion there will be problems at sink nodes. In this paper we provide necessary conditions to verify the feasibility of round robin technique in these networks at the sink nodes by using a technique to regulate multi-radio multichannel assignment. We demonstrate through simulations that dynamic channel assignment scheme using multi-radio, and multichannel configuration at a single sink node can perform close to optimal on the average while multiple sink node assignment also performs well. The methods proposed in this paper can be a valuable tool for network designers in planning network deployment and for optimizing different performance objectives. PMID:22163883

Global Network Connectivity Assessment via Local Data Exchange for Underwater Acoustic Sensor Networks

DTIC Science & Technology

2014-03-31

Network Connectivity Assessment via Local Data Exchange for Underwater Acoustic Sensor Networks M.M. Asadi H. Mahboubi A...2014 Global Network Connectivity Assessment via Local Data Exchange for Underwater Acoustic Sensor Networks Contract Report # AMBUSH.1.1 Contract...pi j /= 0. The sensor network considered in this work is composed of underwater sensors , which use acoustic waves for

Cooperative UAV-Based Communications Backbone for Sensor Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roberts, R S

2001-10-07

The objective of this project is to investigate the use of unmanned air vehicles (UAVs) as mobile, adaptive communications backbones for ground-based sensor networks. In this type of network, the UAVs provide communication connectivity to sensors that cannot communicate with each other because of terrain, distance, or other geographical constraints. In these situations, UAVs provide a vertical communication path for the sensors, thereby mitigating geographic obstacles often imposed on networks. With the proper use of UAVs, connectivity to a widely disbursed sensor network in rugged terrain is readily achieved. Our investigation has focused on networks where multiple cooperating UAVs aremore » used to form a network backbone. The advantage of using multiple UAVs to form the network backbone is parallelization of sensor connectivity. Many widely spaced or isolated sensors can be connected to the network at once using this approach. In these networks, the UAVs logically partition the sensor network into sub-networks (subnets), with one UAV assigned per subnet. Partitioning the network into subnets allows the UAVs to service sensors in parallel thereby decreasing the sensor-to-network connectivity. A UAV services sensors in its subnet by flying a route (path) through the subnet, uplinking data collected by the sensors, and forwarding the data to a ground station. An additional advantage of using multiple UAVs in the network is that they provide redundancy in the communications backbone, so that the failure of a single UAV does not necessarily imply the loss of the network.« less

Dynamic Steering for Improved Sensor Autonomy and Catalogue Maintenance

NASA Astrophysics Data System (ADS)

Hobson, T.; Gordon, N.; Clarkson, I.; Rutten, M.; Bessell, T.

A number of international agencies endeavour to maintain catalogues of the man-made resident space objects (RSOs) currently orbiting the Earth. Such catalogues are primarily created to anticipate and avoid destructive collisions involving important space assets such as manned missions and active satellites. An agencys ability to achieve this objective is dependent on the accuracy, reliability and timeliness of the information used to update its catalogue. A primary means for gathering this information is by regularly making direct observations of the tens-of-thousands of currently detectable RSOs via networks of space surveillance sensors. But operational constraints sometimes prevent accurate and timely reacquisition of all known RSOs, which can cause them to become lost to the tracking system. Furthermore, when comprehensive acquisition of new objects does not occur, these objects, in addition to the lost RSOs, result in uncorrelated detections when next observed. Due to the rising number of space-missions and the introduction of newer, more capable space-sensors, the number of uncorrelated targets is at an all-time high. The process of differentiating uncorrelated detections caused by once-acquired now-lost RSOs from newly detected RSOs is a difficult and often labour intensive task. Current methods for overcoming this challenge focus on advancements in orbit propagation and object characterisation to improve prediction accuracy and target identification. In this paper, we describe a complementary approach that incorporates increased awareness of error and failed observations into the RSO tracking solution. Our methodology employs a technique called dynamic steering to improve the autonomy and capability of a space surveillance networks steerable sensors. By co-situating each sensor with a low-cost high-performance computer, the steerable sensor can quickly and intelligently decide how to steer itself. The sensor-system uses a dedicated parallel-processing architecture to enable it to compute a high-fidelity estimate of the targets prior state error distribution in real-time. Negative information, such as when an RSO is targeted for observation but it is not observed, is incorporated to improve the likelihood of reacquiring the target when attempting to observe the target in future. The sensor is consequently capable of improving its utility by planning each observation using a sensor steering solution that is informed by all prior attempts at observing the target. We describe the practical implementation of a single experimental sensor and offer the results of recent field trials. These trials involved reacquisition and constrained Initial Orbit Determination of RSOs, a number of months after prior observation and initial detection. Using the proposed approach, the system is capable of using targeting information that would be unusable by existing space surveillance networks. The system consequently offers a means of enhancing space surveillance for SSA via increased system capacity, a higher degree of autonomy and the ability to reacquire objects whose dynamics are insufficiently modelled to cue a conventional space surveillance system for observation and tracking.

Game-theoretic homological sensor resource management for SSA

NASA Astrophysics Data System (ADS)

Chin, Sang Peter

2009-05-01

We present a game-theoretic approach to Level 2/3/4 fusion for the purpose of Space Situational Awareness (SSA) along with prototypical SW implementation of this approach to demonstrate its effectiveness for possible future space operations. Our approach is based upon innovative techniques that we are developing to solve dynamic games and Nperson cooperative/non-cooperative games, as well as a new emerging homological sensing algorithms which we apply to control disparate network of space sensors in order to gain better SSA.

Energy efficient data representation and aggregation with event region detection in wireless sensor networks

NASA Astrophysics Data System (ADS)

Banerjee, Torsha

Unlike conventional networks, wireless sensor networks (WSNs) are limited in power, have much smaller memory buffers, and possess relatively slower processing speeds. These characteristics necessitate minimum transfer and storage of information in order to prolong the network lifetime. In this dissertation, we exploit the spatio-temporal nature of sensor data to approximate the current values of the sensors based on readings obtained from neighboring sensors and itself. We propose a Tree based polynomial REGression algorithm, (TREG) that addresses the problem of data compression in wireless sensor networks. Instead of aggregated data, a polynomial function (P) is computed by the regression function, TREG. The coefficients of P are then passed to achieve the following goals: (i) The sink can get attribute values in the regions devoid of sensor nodes, and (ii) Readings over any portion of the region can be obtained at one time by querying the root of the tree. As the size of the data packet from each tree node to its parent remains constant, the proposed scheme scales very well with growing network density or increased coverage area. Since physical attributes exhibit a gradual change over time, we propose an iterative scheme, UPDATE_COEFF, which obviates the need to perform the regression function repeatedly and uses approximations based on previous readings. Extensive simulations are performed on real world data to demonstrate the effectiveness of our proposed aggregation algorithm, TREG. Results reveal that for a network density of 0.0025 nodes/m2, a complete binary tree of depth 4 could provide the absolute error to be less than 6%. A data compression ratio of about 0.02 is achieved using our proposed algorithm, which is almost independent of the tree depth. In addition, our proposed updating scheme makes the aggregation process faster while maintaining the desired error bounds. We also propose a Polynomial-based scheme that addresses the problem of Event Region Detection (PERD) for WSNs. When a single event occurs, a child of the tree sends a Flagged Polynomial (FP) to its parent, if the readings approximated by it falls outside the data range defining the existing phenomenon. After the aggregation process is over, the root having the two polynomials, P and FP can be queried for FP (approximating the new event region) instead of flooding the whole network. For multiple such events, instead of computing a polynomial corresponding to each new event, areas with same data range are combined by the corresponding tree nodes and the aggregated coefficients are passed on. Results reveal that a new event can be detected by PERD while error in detection remains constant and is less than a threshold of 10%. As the node density increases, accuracy and delay for event detection are found to remain almost constant, making PERD highly scalable. Whenever an event occurs in a WSN, data is generated by closeby sensors and relaying the data to the base station (BS) make sensors closer to the BS run out of energy at a much faster rate than sensors in other parts of the network. This gives rise to an unequal distribution of residual energy in the network and makes those sensors with lower remaining energy level die at much faster rate than others. We propose a scheme for enhancing network Lifetime using mobile cluster heads (CH) in a WSN. To maintain remaining energy more evenly, some energy-rich nodes are designated as CHs which move in a controlled manner towards sensors rich in energy and data. This eliminates multihop transmission required by the static sensors and thus increases the overall lifetime of the WSN. We combine the idea of clustering and mobile CH to first form clusters of static sensor nodes. A collaborative strategy among the CHs further increases the lifetime of the network. Time taken for transmitting data to the BS is reduced further by making the CHs follow a connectivity strategy that always maintain a connected path to the BS. Spatial correlation of sensor data can be further exploited for dynamic channel selection in Cellular Communication. In such a scenario within a licensed band, wireless sensors can be deployed (each sensor tuned to a frequency of the channel at a particular time) to sense the interference power of the frequency band. In an ideal channel, interference temperature (IT) which is directly proportional to the interference power, can be assumed to vary spatially with the frequency of the sub channel. We propose a scheme for fitting the sub channel frequencies and corresponding ITs to a regression model for calculating the IT of a random sub channel for further analysis of the channel interference at the base station. Our scheme, based on the readings reported by Sensors helps in Dynamic Channel Selection (S-DCS) in extended C-band for assignment to unlicensed secondary users. S-DCS proves to be economic from energy consumption point of view and it also achieves accuracy with error bound within 6.8%. Again, users are assigned empty sub channels without actually probing them, incurring minimum delay in the process. The overall channel throughput is maximized along with fairness to individual users.

FuGeF: A Resource Bound Secure Forwarding Protocol for Wireless Sensor Networks

PubMed Central

Umar, Idris Abubakar; Mohd Hanapi, Zurina; Sali, A.; Zulkarnain, Zuriati A.

2016-01-01

Resource bound security solutions have facilitated the mitigation of spatio-temporal attacks by altering protocol semantics to provide minimal security while maintaining an acceptable level of performance. The Dynamic Window Secured Implicit Geographic Forwarding (DWSIGF) routing protocol for Wireless Sensor Network (WSN) has been proposed to achieve a minimal selection of malicious nodes by introducing a dynamic collection window period to the protocol’s semantics. However, its selection scheme suffers substantial packet losses due to the utilization of a single distance based parameter for node selection. In this paper, we propose a Fuzzy-based Geographic Forwarding protocol (FuGeF) to minimize packet loss, while maintaining performance. The FuGeF utilizes a new form of dynamism and introduces three selection parameters: remaining energy, connectivity cost, and progressive distance, as well as a Fuzzy Logic System (FLS) for node selection. These introduced mechanisms ensure the appropriate selection of a non-malicious node. Extensive simulation experiments have been conducted to evaluate the performance of the proposed FuGeF protocol as compared to DWSIGF variants. The simulation results show that the proposed FuGeF outperforms the two DWSIGF variants (DWSIGF-P and DWSIGF-R) in terms of packet delivery. PMID:27338411

FuGeF: A Resource Bound Secure Forwarding Protocol for Wireless Sensor Networks.

PubMed

Umar, Idris Abubakar; Mohd Hanapi, Zurina; Sali, A; Zulkarnain, Zuriati A

2016-06-22

Resource bound security solutions have facilitated the mitigation of spatio-temporal attacks by altering protocol semantics to provide minimal security while maintaining an acceptable level of performance. The Dynamic Window Secured Implicit Geographic Forwarding (DWSIGF) routing protocol for Wireless Sensor Network (WSN) has been proposed to achieve a minimal selection of malicious nodes by introducing a dynamic collection window period to the protocol's semantics. However, its selection scheme suffers substantial packet losses due to the utilization of a single distance based parameter for node selection. In this paper, we propose a Fuzzy-based Geographic Forwarding protocol (FuGeF) to minimize packet loss, while maintaining performance. The FuGeF utilizes a new form of dynamism and introduces three selection parameters: remaining energy, connectivity cost, and progressive distance, as well as a Fuzzy Logic System (FLS) for node selection. These introduced mechanisms ensure the appropriate selection of a non-malicious node. Extensive simulation experiments have been conducted to evaluate the performance of the proposed FuGeF protocol as compared to DWSIGF variants. The simulation results show that the proposed FuGeF outperforms the two DWSIGF variants (DWSIGF-P and DWSIGF-R) in terms of packet delivery.

A signal processing framework for simultaneous detection of multiple environmental contaminants

NASA Astrophysics Data System (ADS)

Chakraborty, Subhadeep; Manahan, Michael P.; Mench, Matthew M.

2013-11-01

The possibility of large-scale attacks using chemical warfare agents (CWAs) has exposed the critical need for fundamental research enabling the reliable, unambiguous and early detection of trace CWAs and toxic industrial chemicals. This paper presents a unique approach for the identification and classification of simultaneously present multiple environmental contaminants by perturbing an electrochemical (EC) sensor with an oscillating potential for the extraction of statistically rich information from the current response. The dynamic response, being a function of the degree and mechanism of contamination, is then processed with a symbolic dynamic filter for the extraction of representative patterns, which are then classified using a trained neural network. The approach presented in this paper promises to extend the sensing power and sensitivity of these EC sensors by augmenting and complementing sensor technology with state-of-the-art embedded real-time signal processing capabilities.

Chaotic itinerancy within the coupled dynamics between a physical body and neural oscillator networks

PubMed Central

Mori, Hiroki; Okuyama, Yuji; Asada, Minoru

2017-01-01

Chaotic itinerancy is a phenomenon in which the state of a nonlinear dynamical system spontaneously explores and attracts certain states in a state space. From this perspective, the diverse behavior of animals and its spontaneous transitions lead to a complex coupled dynamical system, including a physical body and a brain. Herein, a series of simulations using different types of non-linear oscillator networks (i.e., regular, small-world, scale-free, random) with a musculoskeletal model (i.e., a snake-like robot) as a physical body are conducted to understand how the chaotic itinerancy of bodily behavior emerges from the coupled dynamics between the body and the brain. A behavior analysis (behavior clustering) and network analysis for the classified behavior are then applied. The former consists of feature vector extraction from the motions and classification of the movement patterns that emerged from the coupled dynamics. The network structures behind the classified movement patterns are revealed by estimating the “information networks” different from the given non-linear oscillator networks based on the transfer entropy which finds the information flow among neurons. The experimental results show that: (1) the number of movement patterns and their duration depend on the sensor ratio to control the balance of strength between the body and the brain dynamics and on the type of the given non-linear oscillator networks; and (2) two kinds of information networks are found behind two kinds movement patterns with different durations by utilizing the complex network measures, clustering coefficient and the shortest path length with a negative and a positive relationship with the duration periods of movement patterns. The current results seem promising for a future extension of the method to a more complicated body and environment. Several requirements are also discussed. PMID:28796797

PageRank versatility analysis of multilayer modality-based network for exploring the evolution of oil-water slug flow.

PubMed

Gao, Zhong-Ke; Dang, Wei-Dong; Li, Shan; Yang, Yu-Xuan; Wang, Hong-Tao; Sheng, Jing-Ran; Wang, Xiao-Fan

2017-07-14

Numerous irregular flow structures exist in the complicated multiphase flow and result in lots of disparate spatial dynamical flow behaviors. The vertical oil-water slug flow continually attracts plenty of research interests on account of its significant importance. Based on the spatial transient flow information acquired through our designed double-layer distributed-sector conductance sensor, we construct multilayer modality-based network to encode the intricate spatial flow behavior. Particularly, we calculate the PageRank versatility and multilayer weighted clustering coefficient to quantitatively explore the inferred multilayer modality-based networks. Our analysis allows characterizing the complicated evolution of oil-water slug flow, from the opening formation of oil slugs, to the succedent inter-collision and coalescence among oil slugs, and then to the dispersed oil bubbles. These properties render our developed method particularly powerful for mining the essential flow features from the multilayer sensor measurements.

A Novel IEEE 802.15.4e DSME MAC for Wireless Sensor Networks

PubMed Central

Sahoo, Prasan Kumar; Pattanaik, Sudhir Ranjan; Wu, Shih-Lin

2017-01-01

IEEE 802.15.4e standard proposes Deterministic and Synchronous Multichannel Extension (DSME) mode for wireless sensor networks (WSNs) to support industrial, commercial and health care applications. In this paper, a new channel access scheme and beacon scheduling schemes are designed for the IEEE 802.15.4e enabled WSNs in star topology to reduce the network discovery time and energy consumption. In addition, a new dynamic guaranteed retransmission slot allocation scheme is designed for devices with the failure Guaranteed Time Slot (GTS) transmission to reduce the retransmission delay. To evaluate our schemes, analytical models are designed to analyze the performance of WSNs in terms of reliability, delay, throughput and energy consumption. Our schemes are validated with simulation and analytical results and are observed that simulation results well match with the analytical one. The evaluated results of our designed schemes can improve the reliability, throughput, delay, and energy consumptions significantly. PMID:28275216

A Novel IEEE 802.15.4e DSME MAC for Wireless Sensor Networks.

PubMed

Sahoo, Prasan Kumar; Pattanaik, Sudhir Ranjan; Wu, Shih-Lin

2017-01-16

IEEE 802.15.4e standard proposes Deterministic and Synchronous Multichannel Extension (DSME) mode for wireless sensor networks (WSNs) to support industrial, commercial and health care applications. In this paper, a new channel access scheme and beacon scheduling schemes are designed for the IEEE 802.15.4e enabled WSNs in star topology to reduce the network discovery time and energy consumption. In addition, a new dynamic guaranteed retransmission slot allocation scheme is designed for devices with the failure Guaranteed Time Slot (GTS) transmission to reduce the retransmission delay. To evaluate our schemes, analytical models are designed to analyze the performance of WSNs in terms of reliability, delay, throughput and energy consumption. Our schemes are validated with simulation and analytical results and are observed that simulation results well match with the analytical one. The evaluated results of our designed schemes can improve the reliability, throughput, delay, and energy consumptions significantly.

A Comprehensive Study of Data Collection Schemes Using Mobile Sinks in Wireless Sensor Networks

PubMed Central

Khan, Abdul Waheed; Abdullah, Abdul Hanan; Anisi, Mohammad Hossein; Bangash, Javed Iqbal

2014-01-01

Recently sink mobility has been exploited in numerous schemes to prolong the lifetime of wireless sensor networks (WSNs). Contrary to traditional WSNs where sensory data from sensor field is ultimately sent to a static sink, mobile sink-based approaches alleviate energy-holes issues thereby facilitating balanced energy consumption among nodes. In mobility scenarios, nodes need to keep track of the latest location of mobile sinks for data delivery. However, frequent propagation of sink topological updates undermines the energy conservation goal and therefore should be controlled. Furthermore, controlled propagation of sinks' topological updates affects the performance of routing strategies thereby increasing data delivery latency and reducing packet delivery ratios. This paper presents a taxonomy of various data collection/dissemination schemes that exploit sink mobility. Based on how sink mobility is exploited in the sensor field, we classify existing schemes into three classes, namely path constrained, path unconstrained, and controlled sink mobility-based schemes. We also organize existing schemes based on their primary goals and provide a comparative study to aid readers in selecting the appropriate scheme in accordance with their particular intended applications and network dynamics. Finally, we conclude our discussion with the identification of some unresolved issues in pursuit of data delivery to a mobile sink. PMID:24504107

Adaptive Self-Tuning Networks

NASA Astrophysics Data System (ADS)

Knox, H. A.; Draelos, T.; Young, C. J.; Lawry, B.; Chael, E. P.; Faust, A.; Peterson, M. G.

2015-12-01

The quality of automatic detections from seismic sensor networks depends on a large number of data processing parameters that interact in complex ways. The largely manual process of identifying effective parameters is painstaking and does not guarantee that the resulting controls are the optimal configuration settings. Yet, achieving superior automatic detection of seismic events is closely related to these parameters. We present an automated sensor tuning (AST) system that learns near-optimal parameter settings for each event type using neuro-dynamic programming (reinforcement learning) trained with historic data. AST learns to test the raw signal against all event-settings and automatically self-tunes to an emerging event in real-time. The overall goal is to reduce the number of missed legitimate event detections and the number of false event detections. Reducing false alarms early in the seismic pipeline processing will have a significant impact on this goal. Applicable both for existing sensor performance boosting and new sensor deployment, this system provides an important new method to automatically tune complex remote sensing systems. Systems tuned in this way will achieve better performance than is currently possible by manual tuning, and with much less time and effort devoted to the tuning process. With ground truth on detections in seismic waveforms from a network of stations, we show that AST increases the probability of detection while decreasing false alarms.

Effects of measurement unobservability on neural extended Kalman filter tracking

NASA Astrophysics Data System (ADS)

Stubberud, Stephen C.; Kramer, Kathleen A.

2009-05-01

An important component of tracking fusion systems is the ability to fuse various sensors into a coherent picture of the scene. When multiple sensor systems are being used in an operational setting, the types of data vary. A significant but often overlooked concern of multiple sensors is the incorporation of measurements that are unobservable. An unobservable measurement is one that may provide information about the state, but cannot recreate a full target state. A line of bearing measurement, for example, cannot provide complete position information. Often, such measurements come from passive sensors such as a passive sonar array or an electronic surveillance measure (ESM) system. Unobservable measurements will, over time, result in the measurement uncertainty to grow without bound. While some tracking implementations have triggers to protect against the detrimental effects, many maneuver tracking algorithms avoid discussing this implementation issue. One maneuver tracking technique is the neural extended Kalman filter (NEKF). The NEKF is an adaptive estimation algorithm that estimates the target track as it trains a neural network on line to reduce the error between the a priori target motion model and the actual target dynamics. The weights of neural network are trained in a similar method to the state estimation/parameter estimation Kalman filter techniques. The NEKF has been shown to improve target tracking accuracy through maneuvers and has been use to predict target behavior using the new model that consists of the a priori model and the neural network. The key to the on-line adaptation of the NEKF is the fact that the neural network is trained using the same residuals as the Kalman filter for the tracker. The neural network weights are treated as augmented states to the target track. Through the state-coupling function, the weights are coupled to the target states. Thus, if the measurements cause the states of the target track to be unobservable, then the weights of the neural network have unobservable modes as well. In recent analysis, the NEKF was shown to have a significantly larger growth in the eigenvalues of the error covariance matrix than the standard EKF tracker when the measurements were purely bearings-only. This caused detrimental effects to the ability of the NEKF to model the target dynamics. In this work, the analysis is expanded to determine the detrimental effects of bearings-only measurements of various uncertainties on the performance of the NEKF when these unobservable measurements are interlaced with completely observable measurements. This analysis provides the ability to put implementation limitations on the NEKF when bearings-only sensors are present.

Achieving Congestion Mitigation Using Distributed Power Control for Spectrum Sensor Nodes in Sensor Network-Aided Cognitive Radio Ad Hoc Networks

PubMed Central

Zhuo, Fan; Duan, Hucai

2017-01-01

The data sequence of spectrum sensing results injected from dedicated spectrum sensor nodes (SSNs) and the data traffic from upstream secondary users (SUs) lead to unpredictable data loads in a sensor network-aided cognitive radio ad hoc network (SN-CRN). As a result, network congestion may occur at a SU acting as fusion center when the offered data load exceeds its available capacity, which degrades network performance. In this paper, we present an effective approach to mitigate congestion of bottlenecked SUs via a proposed distributed power control framework for SSNs over a rectangular grid based SN-CRN, aiming to balance resource load and avoid excessive congestion. To achieve this goal, a distributed power control framework for SSNs from interior tier (IT) and middle tier (MT) is proposed to achieve the tradeoff between channel capacity and energy consumption. In particular, we firstly devise two pricing factors by considering stability of local spectrum sensing and spectrum sensing quality for SSNs. By the aid of pricing factors, the utility function of this power control problem is formulated by jointly taking into account the revenue of power reduction and the cost of energy consumption for IT or MT SSN. By bearing in mind the utility function maximization and linear differential equation constraint of energy consumption, we further formulate the power control problem as a differential game model under a cooperation or noncooperation scenario, and rigorously obtain the optimal solutions to this game model by employing dynamic programming. Then the congestion mitigation for bottlenecked SUs is derived by alleviating the buffer load over their internal buffers. Simulation results are presented to show the effectiveness of the proposed approach under the rectangular grid based SN-CRN scenario. PMID:28914803

Cognitive radio wireless sensor networks: applications, challenges and research trends.

PubMed

Joshi, Gyanendra Prasad; Nam, Seung Yeob; Kim, Sung Won

2013-08-22

A cognitive radio wireless sensor network is one of the candidate areas where cognitive techniques can be used for opportunistic spectrum access. Research in this area is still in its infancy, but it is progressing rapidly. The aim of this study is to classify the existing literature of this fast emerging application area of cognitive radio wireless sensor networks, highlight the key research that has already been undertaken, and indicate open problems. This paper describes the advantages of cognitive radio wireless sensor networks, the difference between ad hoc cognitive radio networks, wireless sensor networks, and cognitive radio wireless sensor networks, potential application areas of cognitive radio wireless sensor networks, challenges and research trend in cognitive radio wireless sensor networks. The sensing schemes suited for cognitive radio wireless sensor networks scenarios are discussed with an emphasis on cooperation and spectrum access methods that ensure the availability of the required QoS. Finally, this paper lists several open research challenges aimed at drawing the attention of the readers toward the important issues that need to be addressed before the vision of completely autonomous cognitive radio wireless sensor networks can be realized.

A Risk Based Approach to Limit the Effects of Covert Channels for Internet Sensor Data Aggregators for Sensor Privacy

NASA Astrophysics Data System (ADS)

Viecco, Camilo H.; Camp, L. Jean

Effective defense against Internet threats requires data on global real time network status. Internet sensor networks provide such real time network data. However, an organization that participates in a sensor network risks providing a covert channel to attackers if that organization’s sensor can be identified. While there is benefit for every party when any individual participates in such sensor deployments, there are perverse incentives against individual participation. As a result, Internet sensor networks currently provide limited data. Ensuring anonymity of individual sensors can decrease the risk of participating in a sensor network without limiting data provision.

Providing IoT Services in Smart Cities through Dynamic Augmented Reality Markers.

PubMed

Chaves-Diéguez, David; Pellitero-Rivero, Alexandre; García-Coego, Daniel; González-Castaño, Francisco Javier; Rodríguez-Hernández, Pedro Salvador; Piñeiro-Gómez, Óscar; Gil-Castiñeira, Felipe; Costa-Montenegro, Enrique

2015-07-03

Smart cities are expected to improve the quality of life of citizens by relying on new paradigms, such as the Internet of Things (IoT) and its capacity to manage and interconnect thousands of sensors and actuators scattered across the city. At the same time, mobile devices widely assist professional and personal everyday activities. A very good example of the potential of these devices for smart cities is their powerful support for intuitive service interfaces (such as those based on augmented reality (AR)) for non-expert users. In our work, we consider a scenario that combines IoT and AR within a smart city maintenance service to improve the accessibility of sensor and actuator devices in the field, where responsiveness is crucial. In it, depending on the location and needs of each service, data and commands will be transported by an urban communications network or consulted on the spot. Direct AR interaction with urban objects has already been described; it usually relies on 2D visual codes to deliver object identifiers (IDs) to the rendering device to identify object resources. These IDs allow information about the objects to be retrieved from a remote server. In this work, we present a novel solution that replaces static AR markers with dynamic markers based on LED communication, which can be decoded through cameras embedded in smartphones. These dynamic markers can directly deliver sensor information to the rendering device, on top of the object ID, without further network interaction.

Providing IoT Services in Smart Cities through Dynamic Augmented Reality Markers

PubMed Central

Chaves-Diéguez, David; Pellitero-Rivero, Alexandre; García-Coego, Daniel; González-Castaño, Francisco Javier; Rodríguez-Hernández, Pedro Salvador; Piñeiro-Gómez, Óscar; Gil-Castiñeira, Felipe; Costa-Montenegro, Enrique

2015-01-01

Smart cities are expected to improve the quality of life of citizens by relying on new paradigms, such as the Internet of Things (IoT) and its capacity to manage and interconnect thousands of sensors and actuators scattered across the city. At the same time, mobile devices widely assist professional and personal everyday activities. A very good example of the potential of these devices for smart cities is their powerful support for intuitive service interfaces (such as those based on augmented reality (AR)) for non-expert users. In our work, we consider a scenario that combines IoT and AR within a smart city maintenance service to improve the accessibility of sensor and actuator devices in the field, where responsiveness is crucial. In it, depending on the location and needs of each service, data and commands will be transported by an urban communications network or consulted on the spot. Direct AR interaction with urban objects has already been described; it usually relies on 2D visual codes to deliver object identifiers (IDs) to the rendering device to identify object resources. These IDs allow information about the objects to be retrieved from a remote server. In this work, we present a novel solution that replaces static AR markers with dynamic markers based on LED communication, which can be decoded through cameras embedded in smartphones. These dynamic markers can directly deliver sensor information to the rendering device, on top of the object ID, without further network interaction. PMID:26151215

Adaptive Management of Computing and Network Resources for Spacecraft Systems

NASA Technical Reports Server (NTRS)

Pfarr, Barbara; Welch, Lonnie R.; Detter, Ryan; Tjaden, Brett; Huh, Eui-Nam; Szczur, Martha R. (Technical Monitor)

2000-01-01

It is likely that NASA's future spacecraft systems will consist of distributed processes which will handle dynamically varying workloads in response to perceived scientific events, the spacecraft environment, spacecraft anomalies and user commands. Since all situations and possible uses of sensors cannot be anticipated during pre-deployment phases, an approach for dynamically adapting the allocation of distributed computational and communication resources is needed. To address this, we are evolving the DeSiDeRaTa adaptive resource management approach to enable reconfigurable ground and space information systems. The DeSiDeRaTa approach embodies a set of middleware mechanisms for adapting resource allocations, and a framework for reasoning about the real-time performance of distributed application systems. The framework and middleware will be extended to accommodate (1) the dynamic aspects of intra-constellation network topologies, and (2) the complete real-time path from the instrument to the user. We are developing a ground-based testbed that will enable NASA to perform early evaluation of adaptive resource management techniques without the expense of first deploying them in space. The benefits of the proposed effort are numerous, including the ability to use sensors in new ways not anticipated at design time; the production of information technology that ties the sensor web together; the accommodation of greater numbers of missions with fewer resources; and the opportunity to leverage the DeSiDeRaTa project's expertise, infrastructure and models for adaptive resource management for distributed real-time systems.

Sensor assignment to mission in AI-TECD

NASA Astrophysics Data System (ADS)

Ganger, Robert; de Mel, Geeth; Pham, Tien; Rudnicki, Ronald; Schreiber, Yonatan

2016-05-01

Sensor-mission assignment involves the allocation of sensors and other information-providing resources to missions in order to cover the information needs of the individual tasks within each mission. The importance of efficient and effective means to find appropriate resources for tasks is exacerbated in the coalition context where the operational environment is dynamic and a multitude of critically important tasks need to achieve their collective goals to meet the objectives of the coalition. The Sensor Assignment to Mission (SAM) framework—a research product of the International Technology Alliance in Network and Information Sciences (NIS-ITA) program—provided the first knowledge intensive resource selection approach for the sensor network domain so that contextual information could be used to effectively select resources for tasks in coalition environments. Recently, CUBRC, Inc. was tasked with operationalizing the SAM framework through the use of the I2WD Common Core Ontologies for the Communications-Electronics Research, Development and Engineering Center (CERDEC) sponsored Actionable Intelligence Technology Enabled Capabilities Demonstration (AI-TECD). The demonstration event took place at Fort Dix, New Jersey during July 2015, and this paper discusses the integration and the successful demonstration of the SAM framework within the AI-TECD, lessons learned, and its potential impact in future operations.

Analysis of the Assignment Scheduling Capability for Unmanned Aerial Vehicles (ASC-U) Simulation Tool

DTIC Science & Technology

2006-06-01

dynamic programming approach known as a “rolling horizon” approach. This method accounts for state transitions within the simulation rather than modeling ... model is based on the framework developed for Dynamic Allocation of Fires and Sensors used to evaluate factors associated with networking assets in the...of UAVs required by all types of maneuver and support brigades. (Witsken, 2004) The Modeling , Virtual Environments, and Simulations Institute

CAOS: the nested catchment soil-vegetation-atmosphere observation platform

NASA Astrophysics Data System (ADS)

Weiler, Markus; Blume, Theresa

2016-04-01

Most catchment based observations linking hydrometeorology, ecohydrology, soil hydrology and hydrogeology are typically not integrated with each other and lack a consistent and appropriate spatial-temporal resolution. Within the research network CAOS (Catchments As Organized Systems), we have initiated and developed a novel and integrated observation platform in several catchments in Luxembourg. In 20 nested catchments covering three distinct geologies the subscale processes at the bedrock-soil-vegetation-atmosphere interface are being monitored at 46 sensor cluster locations. Each sensor cluster is designed to observe a variety of different fluxes and state variables above and below ground, in the saturated and unsaturated zone. The numbers of sensors are chosen to capture the spatial variability as well the average dynamics. At each of these sensor clusters three soil moisture profiles with sensors at different depths, four soil temperature profiles as well as matric potential, air temperature, relative humidity, global radiation, rainfall/throughfall, sapflow and shallow groundwater and stream water levels are measured continuously. In addition, most sensors also measure temperature (water, soil, atmosphere) and electrical conductivity. This setup allows us to determine the local water and energy balance at each of these sites. The discharge gauging sites in the nested catchments are also equipped with automatic water samplers to monitor water quality and water stable isotopes continuously. Furthermore, water temperature and electrical conductivity observations are extended to over 120 locations distributed across the entire stream network to capture the energy exchange between the groundwater, stream water and atmosphere. The measurements at the sensor clusters are complemented by hydrometeorological observations (rain radar, network of distrometers and dense network of precipitation gauges) and linked with high resolution meteorological models. In this presentation, we will highlight the potential of this integrated observation platform to estimate energy and water exchange between the terrestrial and aquatic systems and the atmosphere, to trace water flow pathways in the unsaturated and saturated zone, and to understand the organization of processes and fluxes and thus runoff generation at different temporal and spatial scales.

Modal Frequency Detection in Composite Beams Using Fiber Optic Sensors

DTIC Science & Technology

1997-04-18

Structures 4, 270-280 (1995). [35] Chen-Jung Li and Ray Asok , "Neural Network Representation of Fatigue Damage Dynamics," Smart Materials and Structures 3...37] Roland Ray Kilcher, "Modal Analysis and Impact Damage Assessment of Composite Laminates: an Experimental Study," M.S. thesis, University of

The experimental identification of magnetorheological dampers and evaluation of their controllers

NASA Astrophysics Data System (ADS)

Metered, H.; Bonello, P.; Oyadiji, S. O.

2010-05-01

Magnetorheological (MR) fluid dampers are semi-active control devices that have been applied over a wide range of practical vibration control applications. This paper concerns the experimental identification of the dynamic behaviour of an MR damper and the use of the identified parameters in the control of such a damper. Feed-forward and recurrent neural networks are used to model both the direct and inverse dynamics of the damper. Training and validation of the proposed neural networks are achieved by using the data generated through dynamic tests with the damper mounted on a tensile testing machine. The validation test results clearly show that the proposed neural networks can reliably represent both the direct and inverse dynamic behaviours of an MR damper. The effect of the cylinder's surface temperature on both the direct and inverse dynamics of the damper is studied, and the neural network model is shown to be reasonably robust against significant temperature variation. The inverse recurrent neural network model is introduced as a damper controller and experimentally evaluated against alternative controllers proposed in the literature. The results reveal that the neural-based damper controller offers superior damper control. This observation and the added advantages of low-power requirement, extended service life of the damper and the minimal use of sensors, indicate that a neural-based damper controller potentially offers the most cost-effective vibration control solution among the controllers investigated.

An energy-efficient rate adaptive media access protocol (RA-MAC) for long-lived sensor networks.

PubMed

Hu, Wen; Chen, Quanjun; Corke, Peter; O'Rourke, Damien

2010-01-01

We introduce an energy-efficient Rate Adaptive Media Access Control (RA-MAC) algorithm for long-lived Wireless Sensor Networks (WSNs). Previous research shows that the dynamic and lossy nature of wireless communications is one of the major challenges to reliable data delivery in WSNs. RA-MAC achieves high link reliability in such situations by dynamically trading off data rate for channel gain. The extra gain that can be achieved reduces the packet loss rate which contributes to reduced energy expenditure through a reduced numbers of retransmissions. We achieve this at the expense of raw bit rate which generally far exceeds the application's link requirement. To minimize communication energy consumption, RA-MAC selects the optimal data rate based on the estimated link quality at each data rate and an analytical model of the energy consumption. Our model shows how the selected data rate depends on different channel conditions in order to minimize energy consumption. We have implemented RA-MAC in TinyOS for an off-the-shelf sensor platform (the TinyNode) on top of a state-of-the-art WSN Media Access Control Protocol, SCP-MAC, and evaluated its performance by comparing our implementation with the original SCP-MAC using both simulation and experiment.

A probabilistic dynamic energy model for ad-hoc wireless sensors network with varying topology

NASA Astrophysics Data System (ADS)

Al-Husseini, Amal

In this dissertation we investigate the behavior of Wireless Sensor Networks (WSNs) from the degree distribution and evolution perspective. In specific, we focus on implementation of a scale-free degree distribution topology for energy efficient WSNs. WSNs is an emerging technology that finds its applications in different areas such as environment monitoring, agricultural crop monitoring, forest fire monitoring, and hazardous chemical monitoring in war zones. This technology allows us to collect data without human presence or intervention. Energy conservation/efficiency is one of the major issues in prolonging the active life WSNs. Recently, many energy aware and fault tolerant topology control algorithms have been presented, but there is dearth of research focused on energy conservation/efficiency of WSNs. Therefore, we study energy efficiency and fault-tolerance in WSNs from the degree distribution and evolution perspective. Self-organization observed in natural and biological systems has been directly linked to their degree distribution. It is widely known that scale-free distribution bestows robustness, fault-tolerance, and access efficiency to system. Fascinated by these properties, we propose two complex network theoretic self-organizing models for adaptive WSNs. In particular, we focus on adopting the Barabasi and Albert scale-free model to fit into the constraints and limitations of WSNs. We developed simulation models to conduct numerical experiments and network analysis. The main objective of studying these models is to find ways to reducing energy usage of each node and balancing the overall network energy disrupted by faulty communication among nodes. The first model constructs the wireless sensor network relative to the degree (connectivity) and remaining energy of every individual node. We observed that it results in a scale-free network structure which has good fault tolerance properties in face of random node failures. The second model considers additional constraints on the maximum degree of each node as well as the energy consumption relative to degree changes. This gives more realistic results from a dynamical network perspective. It results in balanced network-wide energy consumption. The results show that networks constructed using the proposed approach have good properties for different centrality measures. The outcomes of the presented research are beneficial to building WSN control models with greater self-organization properties which leads to optimal energy consumption.

Genetically encoded proton sensors reveal activity-dependent pH changes in neurons.

PubMed

Raimondo, Joseph V; Irkle, Agnese; Wefelmeyer, Winnie; Newey, Sarah E; Akerman, Colin J

2012-01-01

The regulation of hydrogen ion concentration (pH) is fundamental to cell viability, metabolism, and enzymatic function. Within the nervous system, the control of pH is also involved in diverse and dynamic processes including development, synaptic transmission, and the control of network excitability. As pH affects neuronal activity, and can also itself be altered by neuronal activity, the existence of tools to accurately measure hydrogen ion fluctuations is important for understanding the role pH plays under physiological and pathological conditions. Outside of their use as a marker of synaptic release, genetically encoded pH sensors have not been utilized to study hydrogen ion fluxes associated with network activity. By combining whole-cell patch clamp with simultaneous two-photon or confocal imaging, we quantified the amplitude and time course of neuronal, intracellular, acidic transients evoked by epileptiform activity in two separate in vitro models of temporal lobe epilepsy. In doing so, we demonstrate the suitability of three genetically encoded pH sensors: deGFP4, E(2)GFP, and Cl-sensor for investigating activity-dependent pH changes at the level of single neurons.

An Embodied Multi-Sensor Fusion Approach to Visual Motion Estimation Using Unsupervised Deep Networks.

PubMed

Shamwell, E Jared; Nothwang, William D; Perlis, Donald

2018-05-04

Aimed at improving size, weight, and power (SWaP)-constrained robotic vision-aided state estimation, we describe our unsupervised, deep convolutional-deconvolutional sensor fusion network, Multi-Hypothesis DeepEfference (MHDE). MHDE learns to intelligently combine noisy heterogeneous sensor data to predict several probable hypotheses for the dense, pixel-level correspondence between a source image and an unseen target image. We show how our multi-hypothesis formulation provides increased robustness against dynamic, heteroscedastic sensor and motion noise by computing hypothesis image mappings and predictions at 76⁻357 Hz depending on the number of hypotheses being generated. MHDE fuses noisy, heterogeneous sensory inputs using two parallel, inter-connected architectural pathways and n (1⁻20 in this work) multi-hypothesis generating sub-pathways to produce n global correspondence estimates between a source and a target image. We evaluated MHDE on the KITTI Odometry dataset and benchmarked it against the vision-only DeepMatching and Deformable Spatial Pyramids algorithms and were able to demonstrate a significant runtime decrease and a performance increase compared to the next-best performing method.

Time Synchronization in Wireless Sensor Networks

DTIC Science & Technology

2003-01-01

University of California Los Angeles Time Synchronization in Wireless Sensor Networks A dissertation submitted in partial satisfaction of the...4. TITLE AND SUBTITLE Time Synchronization in Wireless Sensor Networks 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6...1 1.1 Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Time Synchronization in Sensor Networks

QOS-aware error recovery in wireless body sensor networks using adaptive network coding.

PubMed

Razzaque, Mohammad Abdur; Javadi, Saeideh S; Coulibaly, Yahaya; Hira, Muta Tah

2014-12-29

Wireless body sensor networks (WBSNs) for healthcare and medical applications are real-time and life-critical infrastructures, which require a strict guarantee of quality of service (QoS), in terms of latency, error rate and reliability. Considering the criticality of healthcare and medical applications, WBSNs need to fulfill users/applications and the corresponding network's QoS requirements. For instance, for a real-time application to support on-time data delivery, a WBSN needs to guarantee a constrained delay at the network level. A network coding-based error recovery mechanism is an emerging mechanism that can be used in these systems to support QoS at very low energy, memory and hardware cost. However, in dynamic network environments and user requirements, the original non-adaptive version of network coding fails to support some of the network and user QoS requirements. This work explores the QoS requirements of WBSNs in both perspectives of QoS. Based on these requirements, this paper proposes an adaptive network coding-based, QoS-aware error recovery mechanism for WBSNs. It utilizes network-level and user-/application-level information to make it adaptive in both contexts. Thus, it provides improved QoS support adaptively in terms of reliability, energy efficiency and delay. Simulation results show the potential of the proposed mechanism in terms of adaptability, reliability, real-time data delivery and network lifetime compared to its counterparts.

Smart Rocks for Bridge Scour Monitoring: Design and Localization Using Electromagnetic Techniques and Embedded Orientation Sensors

NASA Astrophysics Data System (ADS)

Radchenko, Andro

River bridge scour is an erosion process in which flowing water removes sediment materials (such as sand, rocks) from a bridge foundation, river beds and banks. As a result, the level of the river bed near a bridge pier is lowering such that the bridge foundation stability can be compromised, and the bridge can collapse. The scour is a dynamic process, which can accelerate rapidly during a flood event. Thus, regular monitoring of the scour progress is necessary to be performed at most river bridges. Present techniques are usually expensive, require large man/hour efforts, and often lack the real-time monitoring capabilities. In this dissertation a new method--'Smart Rocks Network for bridge scour monitoring' is introduced. The method is based on distributed wireless sensors embedded in ground underwater nearby the bridge pillars. The sensor nodes are unconstrained in movement, are equipped with years-lasting batteries and intelligent custom designed electronics, which minimizes power consumption during operation and communication. The electronic part consists of a microcontroller, communication interfaces, orientation and environment sensors (such as are accelerometer, magnetometer, temperature and pressure sensors), supporting power supplies and circuitries. Embedded in the soil nearby a bridge pillar the Smart Rocks can move/drift together with the sediments, and act as the free agent probes transmitting the unique signature signals to the base-station monitors. Individual movement of a Smart Rock can be remotely detected processing the orientation sensors reading. This can give an indication of the on-going scour progress, and set a flag for the on-site inspection. The map of the deployed Smart Rocks Network can be obtained utilizing the custom developed in-network communication protocol with signals intensity (RSSI) analysis. Particle Swarm Optimization (PSO) is applied for map reconstruction. Analysis of the map can provide detailed insight into the scour progress and topology. Smart Rocks Network wireless communication is based on the magnetoinductive (MI) link, at low (125 KHz) frequency, allowing for signal to penetrate through the water, rocks, and the bridge structure. The dissertation describes the Smart Rocks Network implementation, its electronic design and the electromagnetic/computational intelligence techniques used for the network mapping.

Obstacle Avoidance and Target Acquisition for Robot Navigation Using a Mixed Signal Analog/Digital Neuromorphic Processing System

PubMed Central

Milde, Moritz B.; Blum, Hermann; Dietmüller, Alexander; Sumislawska, Dora; Conradt, Jörg; Indiveri, Giacomo; Sandamirskaya, Yulia

2017-01-01

Neuromorphic hardware emulates dynamics of biological neural networks in electronic circuits offering an alternative to the von Neumann computing architecture that is low-power, inherently parallel, and event-driven. This hardware allows to implement neural-network based robotic controllers in an energy-efficient way with low latency, but requires solving the problem of device variability, characteristic for analog electronic circuits. In this work, we interfaced a mixed-signal analog-digital neuromorphic processor ROLLS to a neuromorphic dynamic vision sensor (DVS) mounted on a robotic vehicle and developed an autonomous neuromorphic agent that is able to perform neurally inspired obstacle-avoidance and target acquisition. We developed a neural network architecture that can cope with device variability and verified its robustness in different environmental situations, e.g., moving obstacles, moving target, clutter, and poor light conditions. We demonstrate how this network, combined with the properties of the DVS, allows the robot to avoid obstacles using a simple biologically-inspired dynamics. We also show how a Dynamic Neural Field for target acquisition can be implemented in spiking neuromorphic hardware. This work demonstrates an implementation of working obstacle avoidance and target acquisition using mixed signal analog/digital neuromorphic hardware. PMID:28747883

Obstacle Avoidance and Target Acquisition for Robot Navigation Using a Mixed Signal Analog/Digital Neuromorphic Processing System.

PubMed

Milde, Moritz B; Blum, Hermann; Dietmüller, Alexander; Sumislawska, Dora; Conradt, Jörg; Indiveri, Giacomo; Sandamirskaya, Yulia

2017-01-01

Neuromorphic hardware emulates dynamics of biological neural networks in electronic circuits offering an alternative to the von Neumann computing architecture that is low-power, inherently parallel, and event-driven. This hardware allows to implement neural-network based robotic controllers in an energy-efficient way with low latency, but requires solving the problem of device variability, characteristic for analog electronic circuits. In this work, we interfaced a mixed-signal analog-digital neuromorphic processor ROLLS to a neuromorphic dynamic vision sensor (DVS) mounted on a robotic vehicle and developed an autonomous neuromorphic agent that is able to perform neurally inspired obstacle-avoidance and target acquisition. We developed a neural network architecture that can cope with device variability and verified its robustness in different environmental situations, e.g., moving obstacles, moving target, clutter, and poor light conditions. We demonstrate how this network, combined with the properties of the DVS, allows the robot to avoid obstacles using a simple biologically-inspired dynamics. We also show how a Dynamic Neural Field for target acquisition can be implemented in spiking neuromorphic hardware. This work demonstrates an implementation of working obstacle avoidance and target acquisition using mixed signal analog/digital neuromorphic hardware.

Feature Representations for Neuromorphic Audio Spike Streams.

PubMed

Anumula, Jithendar; Neil, Daniel; Delbruck, Tobi; Liu, Shih-Chii

2018-01-01

Event-driven neuromorphic spiking sensors such as the silicon retina and the silicon cochlea encode the external sensory stimuli as asynchronous streams of spikes across different channels or pixels. Combining state-of-art deep neural networks with the asynchronous outputs of these sensors has produced encouraging results on some datasets but remains challenging. While the lack of effective spiking networks to process the spike streams is one reason, the other reason is that the pre-processing methods required to convert the spike streams to frame-based features needed for the deep networks still require further investigation. This work investigates the effectiveness of synchronous and asynchronous frame-based features generated using spike count and constant event binning in combination with the use of a recurrent neural network for solving a classification task using N-TIDIGITS18 dataset. This spike-based dataset consists of recordings from the Dynamic Audio Sensor, a spiking silicon cochlea sensor, in response to the TIDIGITS audio dataset. We also propose a new pre-processing method which applies an exponential kernel on the output cochlea spikes so that the interspike timing information is better preserved. The results from the N-TIDIGITS18 dataset show that the exponential features perform better than the spike count features, with over 91% accuracy on the digit classification task. This accuracy corresponds to an improvement of at least 2.5% over the use of spike count features, establishing a new state of the art for this dataset.

Eco-hydrological Wireless Sensor Network and upscaling method research in the Heihe River Basin, China

NASA Astrophysics Data System (ADS)

Jin, Rui; kang, Jian

2017-04-01

Wireless Sensor Networks are recognized as one of most important near-surface components of GEOSS (Global Earth Observation System of Systems), with flourish development of low-cost, robust and integrated data loggers and sensors. A nested eco-hydrological wireless sensor network (EHWSN) was installed in the up- and middle-reaches of the Heihe River Basin, operated to obtain multi-scale observation of soil moisture, soil temperature and land surface temperature from 2012 till now. The spatial distribution of EHWSN was optimally designed based on the geo-statistical theory, with the aim to capture the spatial variations and temporal dynamics of soil moisture and soil temperature, and to produce ground truth at grid scale for validating the related remote sensing products and model simulation in the heterogeneous land surface. In terms of upscaling research, we have developed a set of method to aggregate multi-point WSN observations to grid scale ( 1km), including regression kriging estimation to utilize multi-resource remote sensing auxiliary information, block kriging with homogeneous measurement errors, and bayesian-based upscaling algorithm that utilizes MODIS-derived apparent thermal inertia. All the EHWSN observation are organized as datasets to be freely published at http://westdc.westgis.ac.cn/hiwater. EHWSN integrates distributed observation nodes to achieve an automated, intelligent and remote-controllable network that provides superior integrated, standardized and automated observation capabilities for hydrological and ecological processes research at the basin scale.

Feature Representations for Neuromorphic Audio Spike Streams

PubMed Central

Anumula, Jithendar; Neil, Daniel; Delbruck, Tobi; Liu, Shih-Chii

2018-01-01

Event-driven neuromorphic spiking sensors such as the silicon retina and the silicon cochlea encode the external sensory stimuli as asynchronous streams of spikes across different channels or pixels. Combining state-of-art deep neural networks with the asynchronous outputs of these sensors has produced encouraging results on some datasets but remains challenging. While the lack of effective spiking networks to process the spike streams is one reason, the other reason is that the pre-processing methods required to convert the spike streams to frame-based features needed for the deep networks still require further investigation. This work investigates the effectiveness of synchronous and asynchronous frame-based features generated using spike count and constant event binning in combination with the use of a recurrent neural network for solving a classification task using N-TIDIGITS18 dataset. This spike-based dataset consists of recordings from the Dynamic Audio Sensor, a spiking silicon cochlea sensor, in response to the TIDIGITS audio dataset. We also propose a new pre-processing method which applies an exponential kernel on the output cochlea spikes so that the interspike timing information is better preserved. The results from the N-TIDIGITS18 dataset show that the exponential features perform better than the spike count features, with over 91% accuracy on the digit classification task. This accuracy corresponds to an improvement of at least 2.5% over the use of spike count features, establishing a new state of the art for this dataset. PMID:29479300

A novel topology control approach to maintain the node degree in dynamic wireless sensor networks.

PubMed

Huang, Yuanjiang; Martínez, José-Fernán; Díaz, Vicente Hernández; Sendra, Juana

2014-03-07

Topology control is an important technique to improve the connectivity and the reliability of Wireless Sensor Networks (WSNs) by means of adjusting the communication range of wireless sensor nodes. In this paper, a novel Fuzzy-logic Topology Control (FTC) is proposed to achieve any desired average node degree by adaptively changing communication range, thus improving the network connectivity, which is the main target of FTC. FTC is a fully localized control algorithm, and does not rely on location information of neighbors. Instead of designing membership functions and if-then rules for fuzzy-logic controller, FTC is constructed from the training data set to facilitate the design process. FTC is proved to be accurate, stable and has short settling time. In order to compare it with other representative localized algorithms (NONE, FLSS, k-Neighbor and LTRT), FTC is evaluated through extensive simulations. The simulation results show that: firstly, similar to k-Neighbor algorithm, FTC is the best to achieve the desired average node degree as node density varies; secondly, FTC is comparable to FLSS and k-Neighbor in terms of energy-efficiency, but is better than LTRT and NONE; thirdly, FTC has the lowest average maximum communication range than other algorithms, which indicates that the most energy-consuming node in the network consumes the lowest power.

Real-Time Performance of a Self-Powered Environmental IoT Sensor Network System.

PubMed

Wu, Fan; Rüdiger, Christoph; Yuce, Mehmet Rasit

2017-02-01

Wireless sensor networks (WSNs) play an increasingly important role in monitoring applications in many areas. With the emergence of the Internet-of-Things (IoT), many more lowpower sensors will need to be deployed in various environments to collect and monitor data about environmental factors in real time. Providing power supply to these sensor nodes becomes a critical challenge for realizations of IoT applications as sensor nodes are normally battery-powered and have a limited lifetime. This paper proposes a wireless sensor network that is powered by solar energy harvesting. The sensor network monitors the environmental data with low-power sensor electronics and forms a network using multiple XBee wireless modules. A detailed performance analysis of the network system under solar energy harvesting has been presented. The sensor network system and the proposed energy-harvesting techniques are configured to achieve a continuous energy source for the sensor network. The proposed energy-harvesting system has been successfully designed to enable an energy solution in order to keep sensor nodes active and reliable for a whole day. The paper also outlines some of our experiences in real-time implementation of a sensor network system with energy harvesting.

Real-Time Performance of a Self-Powered Environmental IoT Sensor Network System

PubMed Central

Wu, Fan; Rüdiger, Christoph; Yuce, Mehmet Rasit

2017-01-01

Wireless sensor networks (WSNs) play an increasingly important role in monitoring applications in many areas. With the emergence of the Internet-of-Things (IoT), many more low-power sensors will need to be deployed in various environments to collect and monitor data about environmental factors in real time. Providing power supply to these sensor nodes becomes a critical challenge for realizations of IoT applications as sensor nodes are normally battery-powered and have a limited lifetime. This paper proposes a wireless sensor network that is powered by solar energy harvesting. The sensor network monitors the environmental data with low-power sensor electronics and forms a network using multiple XBee wireless modules. A detailed performance analysis of the network system under solar energy harvesting has been presented. The sensor network system and the proposed energy-harvesting techniques are configured to achieve a continuous energy source for the sensor network. The proposed energy-harvesting system has been successfully designed to enable an energy solution in order to keep sensor nodes active and reliable for a whole day. The paper also outlines some of our experiences in real-time implementation of a sensor network system with energy harvesting. PMID:28157148

On the routing protocol influence on the resilience of wireless sensor networks to jamming attacks.

PubMed

Del-Valle-Soto, Carolina; Mex-Perera, Carlos; Monroy, Raul; Nolazco-Flores, Juan Arturo

2015-03-27

In this work, we compare a recently proposed routing protocol, the multi-parent hierarchical (MPH) protocol, with two well-known protocols, the ad hoc on-demand distance vector (AODV) and dynamic source routing (DSR). For this purpose, we have developed a simulator, which faithfully reifies the workings of a given protocol, considering a fixed, reconfigurable ad hoc network given by the number and location of participants, and general network conditions. We consider a scenario that can be found in a large number of wireless sensor network applications, a single sink node that collects all of the information generated by the sensors. The metrics used to compare the protocols were the number of packet retransmissions, carrier sense multiple access (CSMA) inner loop retries, the number of nodes answering the queries from the coordinator (sink) node and the energy consumption. We tested the network under ordinary (without attacks) conditions (and combinations thereof) and when it is subject to different types of jamming attacks (in particular, random and reactive jamming attacks), considering several positions for the jammer. Our results report that MPH has a greater ability to tolerate such attacks than DSR and AODV, since it minimizes and encapsulates the network segment under attack. The self-configuring capabilities of MPH derived from a combination of a proactive routes update, on a periodic-time basis, and a reactive behavior provide higher resilience while offering a better performance (overhead and energy consumption) than AODV and DSR, as shown in our simulation results.

Biologically Inspired SNN for Robot Control.

PubMed

Nichols, Eric; McDaid, Liam J; Siddique, Nazmul

2013-02-01

This paper proposes a spiking-neural-network-based robot controller inspired by the control structures of biological systems. Information is routed through the network using facilitating dynamic synapses with short-term plasticity. Learning occurs through long-term synaptic plasticity which is implemented using the temporal difference learning rule to enable the robot to learn to associate the correct movement with the appropriate input conditions. The network self-organizes to provide memories of environments that the robot encounters. A Pioneer robot simulator with laser and sonar proximity sensors is used to verify the performance of the network with a wall-following task, and the results are presented.

Cognitive Radio Wireless Sensor Networks: Applications, Challenges and Research Trends

PubMed Central

Joshi, Gyanendra Prasad; Nam, Seung Yeob; Kim, Sung Won

2013-01-01

A cognitive radio wireless sensor network is one of the candidate areas where cognitive techniques can be used for opportunistic spectrum access. Research in this area is still in its infancy, but it is progressing rapidly. The aim of this study is to classify the existing literature of this fast emerging application area of cognitive radio wireless sensor networks, highlight the key research that has already been undertaken, and indicate open problems. This paper describes the advantages of cognitive radio wireless sensor networks, the difference between ad hoc cognitive radio networks, wireless sensor networks, and cognitive radio wireless sensor networks, potential application areas of cognitive radio wireless sensor networks, challenges and research trend in cognitive radio wireless sensor networks. The sensing schemes suited for cognitive radio wireless sensor networks scenarios are discussed with an emphasis on cooperation and spectrum access methods that ensure the availability of the required QoS. Finally, this paper lists several open research challenges aimed at drawing the attention of the readers toward the important issues that need to be addressed before the vision of completely autonomous cognitive radio wireless sensor networks can be realized. PMID:23974152

Energy efficient sensor scheduling with a mobile sink node for the target tracking application.

PubMed

Maheswararajah, Suhinthan; Halgamuge, Saman; Premaratne, Malin

2009-01-01

Measurement losses adversely affect the performance of target tracking. The sensor network's life span depends on how efficiently the sensor nodes consume energy. In this paper, we focus on minimizing the total energy consumed by the sensor nodes whilst avoiding measurement losses. Since transmitting data over a long distance consumes a significant amount of energy, a mobile sink node collects the measurements and transmits them to the base station. We assume that the default transmission range of the activated sensor node is limited and it can be increased to maximum range only if the mobile sink node is out-side the default transmission range. Moreover, the active sensor node can be changed after a certain time period. The problem is to select an optimal sensor sequence which minimizes the total energy consumed by the sensor nodes. In this paper, we consider two different problems depend on the mobile sink node's path. First, we assume that the mobile sink node's position is known for the entire time horizon and use the dynamic programming technique to solve the problem. Second, the position of the sink node is varied over time according to a known Markov chain, and the problem is solved by stochastic dynamic programming. We also present sub-optimal methods to solve our problem. A numerical example is presented in order to discuss the proposed methods' performance.

Energy Efficient Sensor Scheduling with a Mobile Sink Node for the Target Tracking Application

PubMed Central

Maheswararajah, Suhinthan; Halgamuge, Saman; Premaratne, Malin

2009-01-01

Measurement losses adversely affect the performance of target tracking. The sensor network's life span depends on how efficiently the sensor nodes consume energy. In this paper, we focus on minimizing the total energy consumed by the sensor nodes whilst avoiding measurement losses. Since transmitting data over a long distance consumes a significant amount of energy, a mobile sink node collects the measurements and transmits them to the base station. We assume that the default transmission range of the activated sensor node is limited and it can be increased to maximum range only if the mobile sink node is out-side the default transmission range. Moreover, the active sensor node can be changed after a certain time period. The problem is to select an optimal sensor sequence which minimizes the total energy consumed by the sensor nodes. In this paper, we consider two different problems depend on the mobile sink node's path. First, we assume that the mobile sink node's position is known for the entire time horizon and use the dynamic programming technique to solve the problem. Second, the position of the sink node is varied over time according to a known Markov chain, and the problem is solved by stochastic dynamic programming. We also present sub-optimal methods to solve our problem. A numerical example is presented in order to discuss the proposed methods' performance PMID:22399934

Accounting for heterogeneity of nutrient dynamics in riverscapes through spatially distributed models

NASA Astrophysics Data System (ADS)

Wollheim, W. M.; Stewart, R. J.

2011-12-01

Numerous types of heterogeneity exist within river systems, leading to hotspots of nutrient sources, sinks, and impacts embedded within an underlying gradient defined by river size. This heterogeneity influences the downstream propagation of anthropogenic impacts across flow conditions. We applied a river network model to explore how nitrogen saturation at river network scales is influenced by the abundance and distribution of potential nutrient processing hotspots (lakes, beaver ponds, tributary junctions, hyporheic zones) under different flow conditions. We determined that under low flow conditions, whole network nutrient removal is relatively insensitive to the number of hotspots because the underlying river network structure has sufficient nutrient processing capacity. However, hotspots become more important at higher flows and greatly influence the spatial distribution of removal within the network at all flows, suggesting that identification of heterogeneity is critical to develop predictive understanding of nutrient removal processes under changing loading and climate conditions. New temporally intensive data from in situ sensors can potentially help to better understand and constrain these dynamics.

An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data.

PubMed

Stromatias, Evangelos; Soto, Miguel; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé

2017-01-01

This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN) System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS) chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77%) and Poker-DVS (100%) real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.

Multi-hop routing mechanism for reliable sensor computing.

PubMed

Chen, Jiann-Liang; Ma, Yi-Wei; Lai, Chia-Ping; Hu, Chia-Cheng; Huang, Yueh-Min

2009-01-01

Current research on routing in wireless sensor computing concentrates on increasing the service lifetime, enabling scalability for large number of sensors and supporting fault tolerance for battery exhaustion and broken nodes. A sensor node is naturally exposed to various sources of unreliable communication channels and node failures. Sensor nodes have many failure modes, and each failure degrades the network performance. This work develops a novel mechanism, called Reliable Routing Mechanism (RRM), based on a hybrid cluster-based routing protocol to specify the best reliable routing path for sensor computing. Table-driven intra-cluster routing and on-demand inter-cluster routing are combined by changing the relationship between clusters for sensor computing. Applying a reliable routing mechanism in sensor computing can improve routing reliability, maintain low packet loss, minimize management overhead and save energy consumption. Simulation results indicate that the reliability of the proposed RRM mechanism is around 25% higher than that of the Dynamic Source Routing (DSR) and ad hoc On-demand Distance Vector routing (AODV) mechanisms.

Application of zonal model on indoor air sensor network design

NASA Astrophysics Data System (ADS)

Chen, Y. Lisa; Wen, Jin

2007-04-01

Growing concerns over the safety of the indoor environment have made the use of sensors ubiquitous. Sensors that detect chemical and biological warfare agents can offer early warning of dangerous contaminants. However, current sensor system design is more informed by intuition and experience rather by systematic design. To develop a sensor system design methodology, a proper indoor airflow modeling approach is needed. Various indoor airflow modeling techniques, from complicated computational fluid dynamics approaches to simplified multi-zone approaches, exist in the literature. In this study, the effects of two airflow modeling techniques, multi-zone modeling technique and zonal modeling technique, on indoor air protection sensor system design are discussed. Common building attack scenarios, using a typical CBW agent, are simulated. Both multi-zone and zonal models are used to predict airflows and contaminant dispersion. Genetic Algorithm is then applied to optimize the sensor location and quantity. Differences in the sensor system design resulting from the two airflow models are discussed for a typical office environment and a large hall environment.

Exploring the "what if?" in geology through a RESTful open-source framework for cloud-based simulation and analysis

NASA Astrophysics Data System (ADS)

Klump, Jens; Robertson, Jess

2016-04-01

The spatial and temporal extent of geological phenomena makes experiments in geology difficult to conduct, if not entirely impossible and collection of data is laborious and expensive - so expensive that most of the time we cannot test a hypothesis. The aim, in many cases, is to gather enough data to build a predictive geological model. Even in a mine, where data are abundant, a model remains incomplete because the information at the level of a blasting block is two orders of magnitude larger than the sample from a drill core, and we have to take measurement errors into account. So, what confidence can we have in a model based on sparse data, uncertainties and measurement error? Our framework consist of two layers: (a) a ground-truth layer that contains geological models, which can be statistically based on historical operations data, and (b) a network of RESTful synthetic sensor microservices which can query the ground-truth for underlying properties and produce a simulated measurement to a control layer, which could be a database or LIMS, a machine learner or a companies' existing data infrastructure. Ground truth data are generated by an implicit geological model which serves as a host for nested models of geological processes as smaller scales. Our two layers are implemented using Flask and Gunicorn, which are open source Python web application framework and server, the PyData stack (numpy, scipy etc) and Rabbit MQ (an open-source queuing library). Sensor data is encoded using a JSON-LD version of the SensorML and Observations and Measurements standards. Containerisation of the synthetic sensors using Docker and CoreOS allows rapid and scalable deployment of large numbers of sensors, as well as sensor discovery to form a self-organized dynamic network of sensors. Real-time simulation of data sources can be used to investigate crucial questions such as the potential information gain from future sensing capabilities, or from new sampling strategies, or the combination of both, and it enables us to test many "what if?" questions, both in geology and in data engineering. What would we be able to see if we could obtain data at higher resolution? How would real-time data analysis change sampling strategies? Does our data infrastructure handle many new real-time data streams? What feature engineering can be deducted for machine learning approaches? By providing a 'data sandbox' able to scale to realistic geological scenarios we hope to start answering some of these questions. Faults happen in real world networks. Future work will investigate the effect of failure on dynamic sensor networks and the impact on the predictive capability of machine learning algorithms.

Determining Methane Leak Locations and Rates with a Wireless Network Composed of Low-Cost, Printed Sensors

NASA Astrophysics Data System (ADS)

Smith, C. J.; Kim, B.; Zhang, Y.; Ng, T. N.; Beck, V.; Ganguli, A.; Saha, B.; Daniel, G.; Lee, J.; Whiting, G.; Meyyappan, M.; Schwartz, D. E.

2015-12-01

We will present our progress on the development of a wireless sensor network that will determine the source and rate of detected methane leaks. The targeted leak detection threshold is 2 g/min with a rate estimation error of 20% and localization error of 1 m within an outdoor area of 100 m2. The network itself is composed of low-cost, high-performance sensor nodes based on printed nanomaterials with expected sensitivity below 1 ppmv methane. High sensitivity to methane is achieved by modifying high surface-area-to-volume-ratio single-walled carbon nanotubes (SWNTs) with materials that adsorb methane molecules. Because the modified SWNTs are not perfectly selective to methane, the sensor nodes contain arrays of variously-modified SWNTs to build diversity of response towards gases with adsorption affinity. Methane selectivity is achieved through advanced pattern-matching algorithms of the array's ensemble response. The system is low power and designed to operate for a year on a single small battery. The SWNT sensing elements consume only microwatts. The largest power consumer is the wireless communication, which provides robust, real-time measurement data. Methane leak localization and rate estimation will be performed by machine-learning algorithms built with the aid of computational fluid dynamics simulations of gas plume formation. This sensor system can be broadly applied at gas wells, distribution systems, refineries, and other downstream facilities. It also can be utilized for industrial and residential safety applications, and adapted to other gases and gas combinations.

An agronomic field-scale sensor network for monitoring soil water and temperature variation

NASA Astrophysics Data System (ADS)

Brown, D. J.; Gasch, C.; Brooks, E. S.; Huggins, D. R.; Campbell, C. S.; Cobos, D. R.

2014-12-01

Environmental sensor networks have been deployed in a variety of contexts to monitor plant, air, water and soil properties. To date, there have been relatively few such networks deployed to monitor dynamic soil properties in cropped fields. Here we report on experience with a distributed soil sensor network that has been deployed for seven years in a research farm with ongoing agronomic field operations. The Washington State University R. J. Cook Agronomy Farm (CAF), Pullman, WA, USA has recently been designated a United States Department of Agriculture (USDA) Long-Term Agro-Ecosystem Research (LTAR) site. In 2007, 12 geo-referenced locations at CAF were instrumented, then in 2009 this network was expended to 42 locations distributed across the 37-ha farm. At each of this locations, Decagon 5TE probes (Decagon Devices Inc., Pullman, WA, USA) were installed at five depths (30, 60, 90, 120, and 150 cm), with temperature and volumetric soil moisture content recorded hourly. Initially, data loggers were wirelessly connected to a data station that could be accessed through a cell connection, but due to the logistics of agronomic field operations, we later buried the dataloggers at each site and now periodically download data via local radio transmission. In this presentation, we share our experience with the installation, maintenance, calibration and data processing associated with an agronomic soil monitoring network. We also present highlights of data derived from this network, including seasonal fluctuations of soil temperature and volumetric water content at each depth, and how these measurements are influenced by crop type, soil properties, landscape position, and precipitation events.

Dependable Wireless Sensor Networks for Prognostics and Health Management: A Survey

DTIC Science & Technology

2014-10-02

sensor network has many advantages. First of all, the absence of wires gives sensor networks the ability to cover a large scale surveillance area...system/component health state. Usually, this information is gathered through independent sensors or a wired network of sensors. The use of a wireless

A reconfigurable computing platform for plume tracking with mobile sensor networks

NASA Astrophysics Data System (ADS)

Kim, Byung Hwa; D'Souza, Colin; Voyles, Richard M.; Hesch, Joel; Roumeliotis, Stergios I.

2006-05-01

Much work has been undertaken recently toward the development of low-power, high-performance sensor networks. There are many static remote sensing applications for which this is appropriate. The focus of this development effort is applications that require higher performance computation, but still involve severe constraints on power and other resources. Toward that end, we are developing a reconfigurable computing platform for miniature robotic and human-deployed sensor systems composed of several mobile nodes. The system provides static and dynamic reconfigurability for both software and hardware by the combination of CPU (central processing unit) and FPGA (field-programmable gate array) allowing on-the-fly reprogrammability. Static reconfigurability of the hardware manifests itself in the form of a "morphing bus" architecture that permits the modular connection of various sensors with no bus interface logic. Dynamic hardware reconfigurability provides for the reallocation of hardware resources at run-time as the mobile, resource-constrained nodes encounter unknown environmental conditions that render various sensors ineffective. This computing platform will be described in the context of work on chemical/biological/radiological plume tracking using a distributed team of mobile sensors. The objective for a dispersed team of ground and/or aerial autonomous vehicles (or hand-carried sensors) is to acquire measurements of the concentration of the chemical agent from optimal locations and estimate its source and spread. This requires appropriate distribution, coordination and communication within the team members across a potentially unknown environment. The key problem is to determine the parameters of the distribution of the harmful agent so as to use these values for determining its source and predicting its spread. The accuracy and convergence rate of this estimation process depend not only on the number and accuracy of the sensor measurements but also on their spatial distribution over time (the sampling strategy). For the safety of a human-deployed distribution of sensors, optimized trajectories to minimize human exposure are also of importance. The systems described in this paper are currently being developed. Parts of the system are already in existence and some results from these are described.

Multipath Routing in Wireless Sensor Networks: Survey and Research Challenges

PubMed Central

Radi, Marjan; Dezfouli, Behnam; Bakar, Kamalrulnizam Abu; Lee, Malrey

2012-01-01

A wireless sensor network is a large collection of sensor nodes with limited power supply and constrained computational capability. Due to the restricted communication range and high density of sensor nodes, packet forwarding in sensor networks is usually performed through multi-hop data transmission. Therefore, routing in wireless sensor networks has been considered an important field of research over the past decade. Nowadays, multipath routing approach is widely used in wireless sensor networks to improve network performance through efficient utilization of available network resources. Accordingly, the main aim of this survey is to present the concept of the multipath routing approach and its fundamental challenges, as well as the basic motivations for utilizing this technique in wireless sensor networks. In addition, we present a comprehensive taxonomy on the existing multipath routing protocols, which are especially designed for wireless sensor networks. We highlight the primary motivation behind the development of each protocol category and explain the operation of different protocols in detail, with emphasis on their advantages and disadvantages. Furthermore, this paper compares and summarizes the state-of-the-art multipath routing techniques from the network application point of view. Finally, we identify open issues for further research in the development of multipath routing protocols for wireless sensor networks. PMID:22368490

Multipath routing in wireless sensor networks: survey and research challenges.

PubMed

Radi, Marjan; Dezfouli, Behnam; Abu Bakar, Kamalrulnizam; Lee, Malrey

2012-01-01

A wireless sensor network is a large collection of sensor nodes with limited power supply and constrained computational capability. Due to the restricted communication range and high density of sensor nodes, packet forwarding in sensor networks is usually performed through multi-hop data transmission. Therefore, routing in wireless sensor networks has been considered an important field of research over the past decade. Nowadays, multipath routing approach is widely used in wireless sensor networks to improve network performance through efficient utilization of available network resources. Accordingly, the main aim of this survey is to present the concept of the multipath routing approach and its fundamental challenges, as well as the basic motivations for utilizing this technique in wireless sensor networks. In addition, we present a comprehensive taxonomy on the existing multipath routing protocols, which are especially designed for wireless sensor networks. We highlight the primary motivation behind the development of each protocol category and explain the operation of different protocols in detail, with emphasis on their advantages and disadvantages. Furthermore, this paper compares and summarizes the state-of-the-art multipath routing techniques from the network application point of view. Finally, we identify open issues for further research in the development of multipath routing protocols for wireless sensor networks.

Event-Based $H_\\infty $ State Estimation for Time-Varying Stochastic Dynamical Networks With State- and Disturbance-Dependent Noises.

PubMed

Sheng, Li; Wang, Zidong; Zou, Lei; Alsaadi, Fuad E

2017-10-01

In this paper, the event-based finite-horizon H ∞ state estimation problem is investigated for a class of discrete time-varying stochastic dynamical networks with state- and disturbance-dependent noises [also called (x,v) -dependent noises]. An event-triggered scheme is proposed to decrease the frequency of the data transmission between the sensors and the estimator, where the signal is transmitted only when certain conditions are satisfied. The purpose of the problem addressed is to design a time-varying state estimator in order to estimate the network states through available output measurements. By employing the completing-the-square technique and the stochastic analysis approach, sufficient conditions are established to ensure that the error dynamics of the state estimation satisfies a prescribed H ∞ performance constraint over a finite horizon. The desired estimator parameters can be designed via solving coupled backward recursive Riccati difference equations. Finally, a numerical example is exploited to demonstrate the effectiveness of the developed state estimation scheme.

Intelligent Sensing and Classification in DSR-Based Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Dempsey, Tae; Sahin, Gokhan; Morton, Yu T. (Jade

Wireless ad hoc networks have fundamentally altered today's battlefield, with applications ranging from unmanned air vehicles to randomly deployed sensor networks. Security and vulnerabilities in wireless ad hoc networks have been considered at different layers, and many attack strategies have been proposed, including denial of service (DoS) through the intelligent jamming of the most critical packet types of flows in a network. This paper investigates the effectiveness of intelligent jamming in wireless ad hoc networks using the Dynamic Source Routing (DSR) and TCP protocols and introduces an intelligent classifier to facilitate the jamming of such networks. Assuming encrypted packet headers and contents, our classifier is based solely on the observable characteristics of size, inter-arrival timing, and direction and classifies packets with up to 99.4% accuracy in our experiments.

Monitoring Aircraft Motion at Airports by LIDAR

NASA Astrophysics Data System (ADS)

Toth, C.; Jozkow, G.; Koppanyi, Z.; Young, S.; Grejner-Brzezinska, D.

2016-06-01

Improving sensor performance, combined with better affordability, provides better object space observability, resulting in new applications. Remote sensing systems are primarily concerned with acquiring data of the static components of our environment, such as the topographic surface of the earth, transportation infrastructure, city models, etc. Observing the dynamic component of the object space is still rather rare in the geospatial application field; vehicle extraction and traffic flow monitoring are a few examples of using remote sensing to detect and model moving objects. Deploying a network of inexpensive LiDAR sensors along taxiways and runways can provide both geometrically and temporally rich geospatial data that aircraft body can be extracted from the point cloud, and then, based on consecutive point clouds motion parameters can be estimated. Acquiring accurate aircraft trajectory data is essential to improve aviation safety at airports. This paper reports about the initial experiences obtained by using a network of four Velodyne VLP- 16 sensors to acquire data along a runway segment.

Urban-scale mapping of PM2.5 distribution via data fusion between high-density sensor network and MODIS Aerosol Optical Depth

NASA Astrophysics Data System (ADS)

Ba, Yu Tao; xian Liu, Bao; Sun, Feng; Wang, Li hua; Tang, Yu jia; Zhang, Da wei

2017-04-01

High-resolution mapping of PM2.5 is the prerequisite for precise analytics and subsequent anti-pollution interventions. Considering the large variances of particulate distribution, urban-scale mapping is challenging either with ground-based fixed stations, with satellites or via models. In this study, a dynamic fusion method between high-density sensor network and MODIS Aerosol Optical Depth (AOD) was introduced. The sensor network was deployed in Beijing ( > 1000 fixed monitors across 16000 km2 area) to provide raw observations with high temporal resolution (sampling interval < 1 hour), high spatial resolution in flat areas ( < 1 km), and low spatial resolution in mountainous areas ( > 5 km). The MODIS AOD was calibrated to provide distribution map with low temporal resolution (daily) and moderate spatial resolution ( = 3 km). By encoding the data quality and defects (e.g. could, reflectance, abnormal), a hybrid interpolation procedure with cross-validation generated PM2.5 distribution with both high temporal and spatial resolution. Several no-pollutant and high-pollution periods were tested to validate the proposed fusion method for capturing the instantaneous patterns of PM2.5 emission.

Indoor Trajectory Tracking Scheme Based on Delaunay Triangulation and Heuristic Information in Wireless Sensor Networks.

PubMed

Qin, Junping; Sun, Shiwen; Deng, Qingxu; Liu, Limin; Tian, Yonghong

2017-06-02

Object tracking and detection is one of the most significant research areas for wireless sensor networks. Existing indoor trajectory tracking schemes in wireless sensor networks are based on continuous localization and moving object data mining. Indoor trajectory tracking based on the received signal strength indicator ( RSSI ) has received increased attention because it has low cost and requires no special infrastructure. However, RSSI tracking introduces uncertainty because of the inaccuracies of measurement instruments and the irregularities (unstable, multipath, diffraction) of wireless signal transmissions in indoor environments. Heuristic information includes some key factors for trajectory tracking procedures. This paper proposes a novel trajectory tracking scheme based on Delaunay triangulation and heuristic information (TTDH). In this scheme, the entire field is divided into a series of triangular regions. The common side of adjacent triangular regions is regarded as a regional boundary. Our scheme detects heuristic information related to a moving object's trajectory, including boundaries and triangular regions. Then, the trajectory is formed by means of a dynamic time-warping position-fingerprint-matching algorithm with heuristic information constraints. Field experiments show that the average error distance of our scheme is less than 1.5 m, and that error does not accumulate among the regions.

Wireless Sensor Networks for Detection of IED Emplacement

DTIC Science & Technology

2009-06-01

unclassified Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 Abstract We are investigating the use of wireless nonimaging -sensor...networks for the difficult problem of detection of suspicious behavior related to IED emplacement. Hardware for surveillance by nonimaging -sensor networks...with people crossing a live sensor network. We conclude that nonimaging -sensor networks can detect a variety of suspicious behavior, but

Unsupervised learning in persistent sensing for target recognition by wireless ad hoc networks of ground-based sensors

NASA Astrophysics Data System (ADS)

Hortos, William S.

2008-04-01

In previous work by the author, effective persistent and pervasive sensing for recognition and tracking of battlefield targets were seen to be achieved, using intelligent algorithms implemented by distributed mobile agents over a composite system of unmanned aerial vehicles (UAVs) for persistence and a wireless network of unattended ground sensors for pervasive coverage of the mission environment. While simulated performance results for the supervised algorithms of the composite system are shown to provide satisfactory target recognition over relatively brief periods of system operation, this performance can degrade by as much as 50% as target dynamics in the environment evolve beyond the period of system operation in which the training data are representative. To overcome this limitation, this paper applies the distributed approach using mobile agents to the network of ground-based wireless sensors alone, without the UAV subsystem, to provide persistent as well as pervasive sensing for target recognition and tracking. The supervised algorithms used in the earlier work are supplanted by unsupervised routines, including competitive-learning neural networks (CLNNs) and new versions of support vector machines (SVMs) for characterization of an unknown target environment. To capture the same physical phenomena from battlefield targets as the composite system, the suite of ground-based sensors can be expanded to include imaging and video capabilities. The spatial density of deployed sensor nodes is increased to allow more precise ground-based location and tracking of detected targets by active nodes. The "swarm" mobile agents enabling WSN intelligence are organized in a three processing stages: detection, recognition and sustained tracking of ground targets. Features formed from the compressed sensor data are down-selected according to an information-theoretic algorithm that reduces redundancy within the feature set, reducing the dimension of samples used in the target recognition and tracking routines. Target tracking is based on simplified versions of Kalman filtration. Accuracy of recognition and tracking of implemented versions of the proposed suite of unsupervised algorithms is somewhat degraded from the ideal. Target recognition and tracking by supervised routines and by unsupervised SVM and CLNN routines in the ground-based WSN is evaluated in simulations using published system values and sensor data from vehicular targets in ground-surveillance scenarios. Results are compared with previously published performance for the system of the ground-based sensor network (GSN) and UAV swarm.

Scheduling Randomly-Deployed Heterogeneous Video Sensor Nodes for Reduced Intrusion Detection Time

NASA Astrophysics Data System (ADS)

Pham, Congduc

This paper proposes to use video sensor nodes to provide an efficient intrusion detection system. We use a scheduling mechanism that takes into account the criticality of the surveillance application and present a performance study of various cover set construction strategies that take into account cameras with heterogeneous angle of view and those with very small angle of view. We show by simulation how a dynamic criticality management scheme can provide fast event detection for mission-critical surveillance applications by increasing the network lifetime and providing low stealth time of intrusions.

A Two-Phase Coverage-Enhancing Algorithm for Hybrid Wireless Sensor Networks.

PubMed

Zhang, Qingguo; Fok, Mable P

2017-01-09

Providing field coverage is a key task in many sensor network applications. In certain scenarios, the sensor field may have coverage holes due to random initial deployment of sensors; thus, the desired level of coverage cannot be achieved. A hybrid wireless sensor network is a cost-effective solution to this problem, which is achieved by repositioning a portion of the mobile sensors in the network to meet the network coverage requirement. This paper investigates how to redeploy mobile sensor nodes to improve network coverage in hybrid wireless sensor networks. We propose a two-phase coverage-enhancing algorithm for hybrid wireless sensor networks. In phase one, we use a differential evolution algorithm to compute the candidate's target positions in the mobile sensor nodes that could potentially improve coverage. In the second phase, we use an optimization scheme on the candidate's target positions calculated from phase one to reduce the accumulated potential moving distance of mobile sensors, such that the exact mobile sensor nodes that need to be moved as well as their final target positions can be determined. Experimental results show that the proposed algorithm provided significant improvement in terms of area coverage rate, average moving distance, area coverage-distance rate and the number of moved mobile sensors, when compare with other approaches.

A Two-Phase Coverage-Enhancing Algorithm for Hybrid Wireless Sensor Networks

PubMed Central

Zhang, Qingguo; Fok, Mable P.

2017-01-01

Providing field coverage is a key task in many sensor network applications. In certain scenarios, the sensor field may have coverage holes due to random initial deployment of sensors; thus, the desired level of coverage cannot be achieved. A hybrid wireless sensor network is a cost-effective solution to this problem, which is achieved by repositioning a portion of the mobile sensors in the network to meet the network coverage requirement. This paper investigates how to redeploy mobile sensor nodes to improve network coverage in hybrid wireless sensor networks. We propose a two-phase coverage-enhancing algorithm for hybrid wireless sensor networks. In phase one, we use a differential evolution algorithm to compute the candidate’s target positions in the mobile sensor nodes that could potentially improve coverage. In the second phase, we use an optimization scheme on the candidate’s target positions calculated from phase one to reduce the accumulated potential moving distance of mobile sensors, such that the exact mobile sensor nodes that need to be moved as well as their final target positions can be determined. Experimental results show that the proposed algorithm provided significant improvement in terms of area coverage rate, average moving distance, area coverage–distance rate and the number of moved mobile sensors, when compare with other approaches. PMID:28075365

Key Exchange Trust Evaluation in Peer-to-Peer Sensor Networks With Unconditionally Secure Key Exchange

NASA Astrophysics Data System (ADS)

Gonzalez, Elias; Kish, Laszlo B.

2016-03-01

As the utilization of sensor networks continue to increase, the importance of security becomes more profound. Many industries depend on sensor networks for critical tasks, and a malicious entity can potentially cause catastrophic damage. We propose a new key exchange trust evaluation for peer-to-peer sensor networks, where part of the network has unconditionally secure key exchange. For a given sensor, the higher the portion of channels with unconditionally secure key exchange the higher the trust value. We give a brief introduction to unconditionally secured key exchange concepts and mention current trust measures in sensor networks. We demonstrate the new key exchange trust measure on a hypothetical sensor network using both wired and wireless communication channels.

Multiple-Optimizing Dynamic Sensor Networks with MIMO Technology (PREPRINT)

DTIC Science & Technology

2010-06-01

a) where PAP is the power consumption dependent on the transmit power , cP is the power consumption dependent on the transceiver circuit...w’s parent p received new’s request, p adds new into its backbone list and sets d(p, new) = 2d; p sends the kowledge it holds to new

Link-state-estimation-based transmission power control in wireless body area networks.

PubMed

Kim, Seungku; Eom, Doo-Seop

2014-07-01

This paper presents a novel transmission power control protocol to extend the lifetime of sensor nodes and to increase the link reliability in wireless body area networks (WBANs). We first experimentally investigate the properties of the link states using the received signal strength indicator (RSSI). We then propose a practical transmission power control protocol based on both short- and long-term link-state estimations. Both the short- and long-term link-state estimations enable the transceiver to adapt the transmission power level and target the RSSI threshold range, respectively, to simultaneously satisfy the requirements of energy efficiency and link reliability. Finally, the performance of the proposed protocol is experimentally evaluated in two experimental scenarios-body posture change and dynamic body motion-and compared with the typical WBAN transmission power control protocols, a real-time reactive scheme, and a dynamic postural position inference mechanism. From the experimental results, it is found that the proposed protocol increases the lifetime of the sensor nodes by a maximum of 9.86% and enhances the link reliability by reducing the packet loss by a maximum of 3.02%.

Dynamic Coordination of Eu-Iminodiacetate to Control Fluorochromic Response of Polymer Hydrogels to Multistimuli.

PubMed

Weng, Gengsheng; Thanneeru, Srinivas; He, Jie

2018-03-01

New fluorochromic materials that reversibly change their emission properties in response to their environment are of interest for the development of sensors and light-emitting materials. A new design of Eu-containing polymer hydrogels showing fast self-healing and tunable fluorochromic properties in response to five different stimuli, including pH, temperature, metal ions, sonication, and force, is reported. The polymer hydrogels are fabricated using Eu-iminodiacetate (IDA) coordination in a hydrophilic poly(N,N-dimethylacrylamide) matrix. Dynamic metal-ligand coordination allows reversible formation and disruption of hydrogel networks under various stimuli which makes hydrogels self-healable and injectable. Such hydrogels show interesting switchable ON/OFF luminescence along with the sol-gel transition through the reversible formation and dissociation of Eu-IDA complexes upon various stimuli. It is demonstrated that Eu-containing hydrogels display fast and reversible mechanochromic response as well in hydrogels having interpenetrating polymer network. Those multistimuli responsive fluorochromic hydrogels illustrate a new pathway to make smart optical materials, particularly for biological sensors where multistimuli response is required. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Multi-mode sensor processing on a dynamically reconfigurable massively parallel processor array

NASA Astrophysics Data System (ADS)

Chen, Paul; Butts, Mike; Budlong, Brad; Wasson, Paul

2008-04-01

This paper introduces a novel computing architecture that can be reconfigured in real time to adapt on demand to multi-mode sensor platforms' dynamic computational and functional requirements. This 1 teraOPS reconfigurable Massively Parallel Processor Array (MPPA) has 336 32-bit processors. The programmable 32-bit communication fabric provides streamlined inter-processor connections with deterministically high performance. Software programmability, scalability, ease of use, and fast reconfiguration time (ranging from microseconds to milliseconds) are the most significant advantages over FPGAs and DSPs. This paper introduces the MPPA architecture, its programming model, and methods of reconfigurability. An MPPA platform for reconfigurable computing is based on a structural object programming model. Objects are software programs running concurrently on hundreds of 32-bit RISC processors and memories. They exchange data and control through a network of self-synchronizing channels. A common application design pattern on this platform, called a work farm, is a parallel set of worker objects, with one input and one output stream. Statically configured work farms with homogeneous and heterogeneous sets of workers have been used in video compression and decompression, network processing, and graphics applications.

Experimental Modal Analysis and Dynaic Strain Fiber Bragg Gratings for Structural Health Monitoring of Composite Aerospace Structures

NASA Astrophysics Data System (ADS)

Panopoulou, A.; Fransen, S.; Gomez Molinero, V.; Kostopoulos, V.

2012-07-01

The objective of this work is to develop a new structural health monitoring system for composite aerospace structures based on dynamic response strain measurements and experimental modal analysis techniques. Fibre Bragg Grating (FBG) optical sensors were used for monitoring the dynamic response of the composite structure. The structural dynamic behaviour has been numerically simulated and experimentally verified by means of vibration testing. The hypothesis of all vibration tests was that actual damage in composites reduces their stiffness and produces the same result as mass increase produces. Thus, damage was simulated by slightly varying locally the mass of the structure at different zones. Experimental modal analysis based on the strain responses was conducted and the extracted strain mode shapes were the input for the damage detection expert system. A feed-forward back propagation neural network was the core of the damage detection system. The features-input to the neural network consisted of the strain mode shapes, extracted from the experimental modal analysis. Dedicated training and validation activities were carried out based on the experimental results. The system showed high reliability, confirmed by the ability of the neural network to recognize the size and the position of damage on the structure. The experiments were performed on a real structure i.e. a lightweight antenna sub- reflector, manufactured and tested at EADS CASA ESPACIO. An integrated FBG sensor network, based on the advantage of multiplexing, was mounted on the structure with optimum topology. Numerical simulation of both structures was used as a support tool at all the steps of the work. Potential applications for the proposed system are during ground qualification extensive tests of space structures and during the mission as modal analysis tool on board, being able via the FBG responses to identify a potential failure.

Artificial Neural Network-Based Early-Age Concrete Strength Monitoring Using Dynamic Response Signals.

PubMed

Kim, Junkyeong; Lee, Chaggil; Park, Seunghee

2017-06-07

Concrete is one of the most common materials used to construct a variety of civil infrastructures. However, since concrete might be susceptible to brittle fracture, it is essential to confirm the strength of concrete at the early-age stage of the curing process to prevent unexpected collapse. To address this issue, this study proposes a novel method to estimate the early-age strength of concrete, by integrating an artificial neural network algorithm with a dynamic response measurement of the concrete material. The dynamic response signals of the concrete, including both electromechanical impedances and guided ultrasonic waves, are obtained from an embedded piezoelectric sensor module. The cross-correlation coefficient of the electromechanical impedance signals and the amplitude of the guided ultrasonic wave signals are selected to quantify the variation in dynamic responses according to the strength of the concrete. Furthermore, an artificial neural network algorithm is used to verify a relationship between the variation in dynamic response signals and concrete strength. The results of an experimental study confirm that the proposed approach can be effectively applied to estimate the strength of concrete material from the early-age stage of the curing process.

Artificial Neural Network-Based Early-Age Concrete Strength Monitoring Using Dynamic Response Signals

PubMed Central

Kim, Junkyeong; Lee, Chaggil; Park, Seunghee

2017-01-01

Concrete is one of the most common materials used to construct a variety of civil infrastructures. However, since concrete might be susceptible to brittle fracture, it is essential to confirm the strength of concrete at the early-age stage of the curing process to prevent unexpected collapse. To address this issue, this study proposes a novel method to estimate the early-age strength of concrete, by integrating an artificial neural network algorithm with a dynamic response measurement of the concrete material. The dynamic response signals of the concrete, including both electromechanical impedances and guided ultrasonic waves, are obtained from an embedded piezoelectric sensor module. The cross-correlation coefficient of the electromechanical impedance signals and the amplitude of the guided ultrasonic wave signals are selected to quantify the variation in dynamic responses according to the strength of the concrete. Furthermore, an artificial neural network algorithm is used to verify a relationship between the variation in dynamic response signals and concrete strength. The results of an experimental study confirm that the proposed approach can be effectively applied to estimate the strength of concrete material from the early-age stage of the curing process. PMID:28590456

Planetary Wind Determination by Doppler Tracking of a Small Entry Probe Network

NASA Astrophysics Data System (ADS)

Atkinson, D. H.; Asmar, S.; Lazio, J.; Preston, R. A.

2017-12-01

To understand the origin and chemical/dynamical evolution of planetary atmospheres, measurements of atmospheric chemistries and processes including dynamics are needed. In situ measurements of planetary winds have been demonstrated on multiple occasions, including the Pioneer multiprobe and Venera missions to Venus, and the Galileo/Jupiter and Huygens/Titan probes. However, with the exception of Pioneer Venus, the retrieval of the zonal (east-west) wind profile has been limited to a single atmospheric slice. significantly improved understanding of the global dynamics requires sampling of multiple latitudes, times of day, and seasons. Simultaneous tracking of a small network of probes would enable measurements of spatially distributed winds providing a substantially improved characterization of a planet's global atmospheric circulation. Careful selection of descent locations would provide wind measurements at latitudes receiving different solar insolations, longitudes reflecting different times of day, and different seasons if both hemispheres are targeted. Doppler wind retrievals are limited by the stability of the probe and carrier spacecraft clocks, and must be equipped with an ultrastable oscillator, accelerometers for reconstructing the probe entry trajectory, and pressure / temperature sensors for determination of descent speed. A probe were equipped with both absolute and dynamic pressure sensors can measure planet center-relative and atmosphere-relative descent speeds, enabling the measurement of vertical winds from convection or atmospheric waves. Possible ambiguities arising from the assumption of no north-south winds could be removed if the probe were simultaneously tracked from the carrier spacecraft as well as from the Earth or a second spacecraft. The global circulation of an atmosphere comprising waves and flows that vary with location and depth is inherently tied to the thermal, chemical, and energy structure of the atmosphere. Wind measurements along a single vertical atmospheric slice cannot adequately represent the overall dynamical properties of the atmosphere. To more completely characterize the dynamical structure of a planetary atmosphere, it is proposed that future in situ planetary missions include a network of small probes dedicated to wind measurements.

Room temperature ammonia vapor sensing properties of transparent single walled carbon nanotube thin film

NASA Astrophysics Data System (ADS)

Shobin, L. R.; Manivannan, S.

2014-10-01

Carbon nanotube (CNT) networks are identified as potential substitute and surpass the conventional indium doped tin oxide (ITO) in transparent conducting electrodes, thin-film transistors, solar cells, and chemical sensors. Among them, CNT based gas sensors gained more interest because of its need in environmental monitoring, industrial control, and detection of gases in warfare or for averting security threats. The unique properties of CNT networks such as high surface area, low density, high thermal conductivity and chemical sensitivity making them as a potential candidate for gas sensing applications. Commercial unsorted single walled carbon nanotubes (SWCNT) were purified by thermal oxidation and acid treatment processes and dispersed in organic solvent N-methyl pyrolidone using sonication process in the absence of polymer or surfactant. Optically transparent SWCNT networks are realized on glass substrate by coating the dispersed SWCNT with the help of dynamic spray coating process at 200ºC. The SWCNT random network was characterized by scanning electron microscopy and UV-vis-NIR spectroscopy. Gas sensing property of transparent film towards ammonia vapor is studied at room temperature by measuring the resistance change with respect to the concentration in the range 0-1000 ppm. The sensor response is increased logarithmically in the concentration range 0 to 1000 ppm with the detection limit 0.007 ppm. The random networks are able to detect ammonia vapor selectively because of the high electron donating nature of ammonia molecule to the SWCNT. The sensor is reversible and selective to ammonia vapor with response time 70 seconds and recovery time 423 seconds for 62.5 ppm with 90% optical transparency at 550 nm.

Real-Time Alpine Measurement System Using Wireless Sensor Networks

PubMed Central

2017-01-01

Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributed Wireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra’s wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km2 network location. Combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape. PMID:29120376

Real-Time Alpine Measurement System Using Wireless Sensor Networks.

PubMed

Malek, Sami A; Avanzi, Francesco; Brun-Laguna, Keoma; Maurer, Tessa; Oroza, Carlos A; Hartsough, Peter C; Watteyne, Thomas; Glaser, Steven D

2017-11-09

Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributed Wireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra's wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km 2 network location. Combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape.

Random and Directed Walk-Based Top-k Queries in Wireless Sensor Networks

PubMed Central

Fu, Jun-Song; Liu, Yun

2015-01-01

In wireless sensor networks, filter-based top-k query approaches are the state-of-the-art solutions and have been extensively researched in the literature, however, they are very sensitive to the network parameters, including the size of the network, dynamics of the sensors’ readings and declines in the overall range of all the readings. In this work, a random walk-based top-k query approach called RWTQ and a directed walk-based top-k query approach called DWTQ are proposed. At the beginning of a top-k query, one or several tokens are sent to the specific node(s) in the network by the base station. Then, each token walks in the network independently to record and process the readings in a random or directed way. A strategy of choosing the “right” way in DWTQ is carefully designed for the token(s) to arrive at the high-value regions as soon as possible. When designing the walking strategy for DWTQ, the spatial correlations of the readings are also considered. Theoretical analysis and simulation results indicate that RWTQ and DWTQ both are very robust against these parameters discussed previously. In addition, DWTQ outperforms TAG, FILA and EXTOK in transmission cost, energy consumption and network lifetime. PMID:26016914

A Symbolic and Graphical Computer Representation of Dynamical Systems

NASA Astrophysics Data System (ADS)

Gould, Laurence I.

2005-04-01

AUTONO is a Macsyma/Maxima program, designed at the University of Hartford, for solving autonomous systems of differential equations as well as for relating Lagrangians and Hamiltonians to their associated dynamical equations. AUTONO can be used in a number of fields to decipher a variety of complex dynamical systems with ease, producing their Lagrangian and Hamiltonian equations in seconds. These equations can then be incorporated into VisSim, a modeling and simulation program, which yields graphical representations of motion in a given system through easily chosen input parameters. The program, along with the VisSim differential-equations graphical package, allows for resolution and easy understanding of complex problems in a relatively short time; thus enabling quicker and more advanced computing of dynamical systems on any number of platforms---from a network of sensors on a space probe, to the behavior of neural networks, to the effects of an electromagnetic field on components in a dynamical system. A flowchart of AUTONO, along with some simple applications and VisSim output, will be shown.

Adaptive Self Tuning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peterson, Matthew; Draelos, Timothy; Knox, Hunter

2017-05-02

The AST software includes numeric methods to 1) adjust STA/LTA signal detector trigger level (TL) values and 2) filter detections for a network of sensors. AST adapts TL values to the current state of the environment by leveraging cooperation within a neighborhood of sensors. The key metric that guides the dynamic tuning is consistency of each sensor with its nearest neighbors: TL values are automatically adjusted on a per station basis to be more or less sensitive to produce consistent agreement of detections in its neighborhood. The AST algorithm adapts in near real-time to changing conditions in an attempt tomore » automatically self-tune a signal detector to identify (detect) only signals from events of interest.« less

Three-dimensional ocean sensor networks: A survey

NASA Astrophysics Data System (ADS)

Wang, Yu; Liu, Yingjian; Guo, Zhongwen

2012-12-01

The past decade has seen a growing interest in ocean sensor networks because of their wide applications in marine research, oceanography, ocean monitoring, offshore exploration, and defense or homeland security. Ocean sensor networks are generally formed with various ocean sensors, autonomous underwater vehicles, surface stations, and research vessels. To make ocean sensor network applications viable, efficient communication among all devices and components is crucial. Due to the unique characteristics of underwater acoustic channels and the complex deployment environment in three dimensional (3D) ocean spaces, new efficient and reliable communication and networking protocols are needed in design of ocean sensor networks. In this paper, we aim to provide an overview of the most recent advances in network design principles for 3D ocean sensor networks, with focuses on deployment, localization, topology design, and position-based routing in 3D ocean spaces.

Availability Issues in Wireless Visual Sensor Networks

PubMed Central

Costa, Daniel G.; Silva, Ivanovitch; Guedes, Luiz Affonso; Vasques, Francisco; Portugal, Paulo

2014-01-01

Wireless visual sensor networks have been considered for a large set of monitoring applications related with surveillance, tracking and multipurpose visual monitoring. When sensors are deployed over a monitored field, permanent faults may happen during the network lifetime, reducing the monitoring quality or rendering parts or the entire network unavailable. In a different way from scalar sensor networks, camera-enabled sensors collect information following a directional sensing model, which changes the notions of vicinity and redundancy. Moreover, visual source nodes may have different relevancies for the applications, according to the monitoring requirements and cameras' poses. In this paper we discuss the most relevant availability issues related to wireless visual sensor networks, addressing availability evaluation and enhancement. Such discussions are valuable when designing, deploying and managing wireless visual sensor networks, bringing significant contributions to these networks. PMID:24526301

Seamless Tracing of Human Behavior Using Complementary Wearable and House-Embedded Sensors

PubMed Central

Augustyniak, Piotr; Smoleń, Magdalena; Mikrut, Zbigniew; Kańtoch, Eliasz

2014-01-01

This paper presents a multimodal system for seamless surveillance of elderly people in their living environment. The system uses simultaneously a wearable sensor network for each individual and premise-embedded sensors specific for each environment. The paper demonstrates the benefits of using complementary information from two types of mobility sensors: visual flow-based image analysis and an accelerometer-based wearable network. The paper provides results for indoor recognition of several elementary poses and outdoor recognition of complex movements. Instead of complete system description, particular attention was drawn to a polar histogram-based method of visual pose recognition, complementary use and synchronization of the data from wearable and premise-embedded networks and an automatic danger detection algorithm driven by two premise- and subject-related databases. The novelty of our approach also consists in feeding the databases with real-life recordings from the subject, and in using the dynamic time-warping algorithm for measurements of distance between actions represented as elementary poses in behavioral records. The main results of testing our method include: 95.5% accuracy of elementary pose recognition by the video system, 96.7% accuracy of elementary pose recognition by the accelerometer-based system, 98.9% accuracy of elementary pose recognition by the combined accelerometer and video-based system, and 80% accuracy of complex outdoor activity recognition by the accelerometer-based wearable system. PMID:24787640

Operating Systems for Wireless Sensor Networks: A Survey

PubMed Central

Farooq, Muhammad Omer; Kunz, Thomas

2011-01-01

This paper presents a survey on the current state-of-the-art in Wireless Sensor Network (WSN) Operating Systems (OSs). In recent years, WSNs have received tremendous attention in the research community, with applications in battlefields, industrial process monitoring, home automation, and environmental monitoring, to name but a few. A WSN is a highly dynamic network because nodes die due to severe environmental conditions and battery power depletion. Furthermore, a WSN is composed of miniaturized motes equipped with scarce resources e.g., limited memory and computational abilities. WSNs invariably operate in an unattended mode and in many scenarios it is impossible to replace sensor motes after deployment, therefore a fundamental objective is to optimize the sensor motes’ life time. These characteristics of WSNs impose additional challenges on OS design for WSN, and consequently, OS design for WSN deviates from traditional OS design. The purpose of this survey is to highlight major concerns pertaining to OS design in WSNs and to point out strengths and weaknesses of contemporary OSs for WSNs, keeping in mind the requirements of emerging WSN applications. The state-of-the-art in operating systems for WSNs has been examined in terms of the OS Architecture, Programming Model, Scheduling, Memory Management and Protection, Communication Protocols, Resource Sharing, Support for Real-Time Applications, and additional features. These features are surveyed for both real-time and non-real-time WSN operating systems. PMID:22163934

Operating systems for wireless sensor networks: a survey.

PubMed

Farooq, Muhammad Omer; Kunz, Thomas

2011-01-01

This paper presents a survey on the current state-of-the-art in Wireless Sensor Network (WSN) Operating Systems (OSs). In recent years, WSNs have received tremendous attention in the research community, with applications in battlefields, industrial process monitoring, home automation, and environmental monitoring, to name but a few. A WSN is a highly dynamic network because nodes die due to severe environmental conditions and battery power depletion. Furthermore, a WSN is composed of miniaturized motes equipped with scarce resources e.g., limited memory and computational abilities. WSNs invariably operate in an unattended mode and in many scenarios it is impossible to replace sensor motes after deployment, therefore a fundamental objective is to optimize the sensor motes' life time. These characteristics of WSNs impose additional challenges on OS design for WSN, and consequently, OS design for WSN deviates from traditional OS design. The purpose of this survey is to highlight major concerns pertaining to OS design in WSNs and to point out strengths and weaknesses of contemporary OSs for WSNs, keeping in mind the requirements of emerging WSN applications. The state-of-the-art in operating systems for WSNs has been examined in terms of the OS Architecture, Programming Model, Scheduling, Memory Management and Protection, Communication Protocols, Resource Sharing, Support for Real-Time Applications, and additional features. These features are surveyed for both real-time and non-real-time WSN operating systems.

Autonomic and Coevolutionary Sensor Networking

NASA Astrophysics Data System (ADS)

Boonma, Pruet; Suzuki, Junichi

(WSNs) applications are often required to balance the tradeoffs among conflicting operational objectives (e.g., latency and power consumption) and operate at an optimal tradeoff. This chapter proposes and evaluates a architecture, called BiSNET/e, which allows WSN applications to overcome this issue. BiSNET/e is designed to support three major types of WSN applications: , and hybrid applications. Each application is implemented as a decentralized group of, which is analogous to a bee colony (application) consisting of bees (agents). Agents collect sensor data or detect an event (a significant change in sensor reading) on individual nodes, and carry sensor data to base stations. They perform these data collection and event detection functionalities by sensing their surrounding network conditions and adaptively invoking behaviors such as pheromone emission, reproduction, migration, swarming and death. Each agent has its own behavior policy, as a set of genes, which defines how to invoke its behaviors. BiSNET/e allows agents to evolve their behavior policies (genes) across generations and autonomously adapt their performance to given objectives. Simulation results demonstrate that, in all three types of applications, agents evolve to find optimal tradeoffs among conflicting objectives and adapt to dynamic network conditions such as traffic fluctuations and node failures/additions. Simulation results also illustrate that, in hybrid applications, data collection agents and event detection agents coevolve to augment their adaptability and performance.

A comparative study of wireless sensor networks and their routing protocols.

PubMed

Bhattacharyya, Debnath; Kim, Tai-hoon; Pal, Subhajit

2010-01-01

Recent developments in the area of micro-sensor devices have accelerated advances in the sensor networks field leading to many new protocols specifically designed for wireless sensor networks (WSNs). Wireless sensor networks with hundreds to thousands of sensor nodes can gather information from an unattended location and transmit the gathered data to a particular user, depending on the application. These sensor nodes have some constraints due to their limited energy, storage capacity and computing power. Data are routed from one node to other using different routing protocols. There are a number of routing protocols for wireless sensor networks. In this review article, we discuss the architecture of wireless sensor networks. Further, we categorize the routing protocols according to some key factors and summarize their mode of operation. Finally, we provide a comparative study on these various protocols.

A Quantitative Risk Assessment Model Involving Frequency and Threat Degree under Line-of-Business Services for Infrastructure of Emerging Sensor Networks.

PubMed

Jing, Xu; Hu, Hanwen; Yang, Huijun; Au, Man Ho; Li, Shuqin; Xiong, Naixue; Imran, Muhammad; Vasilakos, Athanasios V

2017-03-21

The prospect of Line-of-Business Services (LoBSs) for infrastructure of Emerging Sensor Networks (ESNs) is exciting. Access control remains a top challenge in this scenario as the service provider's server contains a lot of valuable resources. LoBSs' users are very diverse as they may come from a wide range of locations with vastly different characteristics. Cost of joining could be low and in many cases, intruders are eligible users conducting malicious actions. As a result, user access should be adjusted dynamically. Assessing LoBSs' risk dynamically based on both frequency and threat degree of malicious operations is therefore necessary. In this paper, we proposed a Quantitative Risk Assessment Model (QRAM) involving frequency and threat degree based on value at risk. To quantify the threat degree as an elementary intrusion effort, we amend the influence coefficient of risk indexes in the network security situation assessment model. To quantify threat frequency as intrusion trace effort, we make use of multiple behavior information fusion. Under the influence of intrusion trace, we adapt the historical simulation method of value at risk to dynamically access LoBSs' risk. Simulation based on existing data is used to select appropriate parameters for QRAM. Our simulation results show that the duration influence on elementary intrusion effort is reasonable when the normalized parameter is 1000. Likewise, the time window of intrusion trace and the weight between objective risk and subjective risk can be set to 10 s and 0.5, respectively. While our focus is to develop QRAM for assessing the risk of LoBSs for infrastructure of ESNs dynamically involving frequency and threat degree, we believe it is also appropriate for other scenarios in cloud computing.

A Quantitative Risk Assessment Model Involving Frequency and Threat Degree under Line-of-Business Services for Infrastructure of Emerging Sensor Networks

PubMed Central

Jing, Xu; Hu, Hanwen; Yang, Huijun; Au, Man Ho; Li, Shuqin; Xiong, Naixue; Imran, Muhammad; Vasilakos, Athanasios V.

2017-01-01

The prospect of Line-of-Business Services (LoBSs) for infrastructure of Emerging Sensor Networks (ESNs) is exciting. Access control remains a top challenge in this scenario as the service provider’s server contains a lot of valuable resources. LoBSs’ users are very diverse as they may come from a wide range of locations with vastly different characteristics. Cost of joining could be low and in many cases, intruders are eligible users conducting malicious actions. As a result, user access should be adjusted dynamically. Assessing LoBSs’ risk dynamically based on both frequency and threat degree of malicious operations is therefore necessary. In this paper, we proposed a Quantitative Risk Assessment Model (QRAM) involving frequency and threat degree based on value at risk. To quantify the threat degree as an elementary intrusion effort, we amend the influence coefficient of risk indexes in the network security situation assessment model. To quantify threat frequency as intrusion trace effort, we make use of multiple behavior information fusion. Under the influence of intrusion trace, we adapt the historical simulation method of value at risk to dynamically access LoBSs’ risk. Simulation based on existing data is used to select appropriate parameters for QRAM. Our simulation results show that the duration influence on elementary intrusion effort is reasonable when the normalized parameter is 1000. Likewise, the time window of intrusion trace and the weight between objective risk and subjective risk can be set to 10 s and 0.5, respectively. While our focus is to develop QRAM for assessing the risk of LoBSs for infrastructure of ESNs dynamically involving frequency and threat degree, we believe it is also appropriate for other scenarios in cloud computing. PMID:28335569

Wearable Device-Based Gait Recognition Using Angle Embedded Gait Dynamic Images and a Convolutional Neural Network.

PubMed

Zhao, Yongjia; Zhou, Suiping

2017-02-28

The widespread installation of inertial sensors in smartphones and other wearable devices provides a valuable opportunity to identify people by analyzing their gait patterns, for either cooperative or non-cooperative circumstances. However, it is still a challenging task to reliably extract discriminative features for gait recognition with noisy and complex data sequences collected from casually worn wearable devices like smartphones. To cope with this problem, we propose a novel image-based gait recognition approach using the Convolutional Neural Network (CNN) without the need to manually extract discriminative features. The CNN's input image, which is encoded straightforwardly from the inertial sensor data sequences, is called Angle Embedded Gait Dynamic Image (AE-GDI). AE-GDI is a new two-dimensional representation of gait dynamics, which is invariant to rotation and translation. The performance of the proposed approach in gait authentication and gait labeling is evaluated using two datasets: (1) the McGill University dataset, which is collected under realistic conditions; and (2) the Osaka University dataset with the largest number of subjects. Experimental results show that the proposed approach achieves competitive recognition accuracy over existing approaches and provides an effective parametric solution for identification among a large number of subjects by gait patterns.

Wearable Device-Based Gait Recognition Using Angle Embedded Gait Dynamic Images and a Convolutional Neural Network

PubMed Central

Zhao, Yongjia; Zhou, Suiping

2017-01-01

The widespread installation of inertial sensors in smartphones and other wearable devices provides a valuable opportunity to identify people by analyzing their gait patterns, for either cooperative or non-cooperative circumstances. However, it is still a challenging task to reliably extract discriminative features for gait recognition with noisy and complex data sequences collected from casually worn wearable devices like smartphones. To cope with this problem, we propose a novel image-based gait recognition approach using the Convolutional Neural Network (CNN) without the need to manually extract discriminative features. The CNN’s input image, which is encoded straightforwardly from the inertial sensor data sequences, is called Angle Embedded Gait Dynamic Image (AE-GDI). AE-GDI is a new two-dimensional representation of gait dynamics, which is invariant to rotation and translation. The performance of the proposed approach in gait authentication and gait labeling is evaluated using two datasets: (1) the McGill University dataset, which is collected under realistic conditions; and (2) the Osaka University dataset with the largest number of subjects. Experimental results show that the proposed approach achieves competitive recognition accuracy over existing approaches and provides an effective parametric solution for identification among a large number of subjects by gait patterns. PMID:28264503

Underwater Sensor Nodes and Networks

PubMed Central

Lloret, Jaime

2013-01-01

Sensor technology has matured enough to be used in any type of environment. The appearance of new physical sensors has increased the range of environmental parameters for gathering data. Because of the huge amount of unexploited resources in the ocean environment, there is a need of new research in the field of sensors and sensor networks. This special issue is focused on collecting recent advances on underwater sensors and underwater sensor networks in order to measure, monitor, surveillance of and control of underwater environments. On the one hand, from the sensor node perspective, we will see works related with the deployment of physical sensors, development of sensor nodes and transceivers for sensor nodes, sensor measurement analysis and several issues such as layer 1 and 2 protocols for underwater communication and sensor localization and positioning systems. On the other hand, from the sensor network perspective, we will see several architectures and protocols for underwater environments and analysis concerning sensor network measurements. Both sides will provide us a complete view of last scientific advances in this research field. PMID:24013489

Cross-layer protocol design for QoS optimization in real-time wireless sensor networks

NASA Astrophysics Data System (ADS)

Hortos, William S.

2010-04-01

The metrics of quality of service (QoS) for each sensor type in a wireless sensor network can be associated with metrics for multimedia that describe the quality of fused information, e.g., throughput, delay, jitter, packet error rate, information correlation, etc. These QoS metrics are typically set at the highest, or application, layer of the protocol stack to ensure that performance requirements for each type of sensor data are satisfied. Application-layer metrics, in turn, depend on the support of the lower protocol layers: session, transport, network, data link (MAC), and physical. The dependencies of the QoS metrics on the performance of the higher layers of the Open System Interconnection (OSI) reference model of the WSN protocol, together with that of the lower three layers, are the basis for a comprehensive approach to QoS optimization for multiple sensor types in a general WSN model. The cross-layer design accounts for the distributed power consumption along energy-constrained routes and their constituent nodes. Following the author's previous work, the cross-layer interactions in the WSN protocol are represented by a set of concatenated protocol parameters and enabling resource levels. The "best" cross-layer designs to achieve optimal QoS are established by applying the general theory of martingale representations to the parameterized multivariate point processes (MVPPs) for discrete random events occurring in the WSN. Adaptive control of network behavior through the cross-layer design is realized through the parametric factorization of the stochastic conditional rates of the MVPPs. The cross-layer protocol parameters for optimal QoS are determined in terms of solutions to stochastic dynamic programming conditions derived from models of transient flows for heterogeneous sensor data and aggregate information over a finite time horizon. Markov state processes, embedded within the complex combinatorial history of WSN events, are more computationally tractable and lead to simplifications for any simulated or analytical performance evaluations of the cross-layer designs.

Development and Implementation of Low-Cost Mobile Sensor Platforms Within a Wireless Sensor Network

DTIC Science & Technology

2010-09-01

WIRELESS SENSOR NETWORK by Michael Jay Tozzi September 2010 Thesis Advisor: Rachel Goshorn Second Reader: Duane Davis Approved for...Platforms Within a Wireless Sensor Network 6. AUTHOR(S) Tozzi, Michael Jay 5. FUNDING NUMBERS 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Naval...IMPLEMENTATION OF LOW-COST MOBILE SENSOR PLATFORMS WITHIN A WIRELESS SENSOR NETWORK Michael Jay Tozzi Lieutenant, United States Navy B.S., United

D-MSR: a distributed network management scheme for real-time monitoring and process control applications in wireless industrial automation.

PubMed

Zand, Pouria; Dilo, Arta; Havinga, Paul

2013-06-27

Current wireless technologies for industrial applications, such as WirelessHART and ISA100.11a, use a centralized management approach where a central network manager handles the requirements of the static network. However, such a centralized approach has several drawbacks. For example, it cannot cope with dynamicity/disturbance in large-scale networks in a real-time manner and it incurs a high communication overhead and latency for exchanging management traffic. In this paper, we therefore propose a distributed network management scheme, D-MSR. It enables the network devices to join the network, schedule their communications, establish end-to-end connections by reserving the communication resources for addressing real-time requirements, and cope with network dynamicity (e.g., node/edge failures) in a distributed manner. According to our knowledge, this is the first distributed management scheme based on IEEE 802.15.4e standard, which guides the nodes in different phases from joining until publishing their sensor data in the network. We demonstrate via simulation that D-MSR can address real-time and reliable communication as well as the high throughput requirements of industrial automation wireless networks, while also achieving higher efficiency in network management than WirelessHART, in terms of delay and overhead.

D-MSR: A Distributed Network Management Scheme for Real-Time Monitoring and Process Control Applications in Wireless Industrial Automation

PubMed Central

Zand, Pouria; Dilo, Arta; Havinga, Paul

2013-01-01

Current wireless technologies for industrial applications, such as WirelessHART and ISA100.11a, use a centralized management approach where a central network manager handles the requirements of the static network. However, such a centralized approach has several drawbacks. For example, it cannot cope with dynamicity/disturbance in large-scale networks in a real-time manner and it incurs a high communication overhead and latency for exchanging management traffic. In this paper, we therefore propose a distributed network management scheme, D-MSR. It enables the network devices to join the network, schedule their communications, establish end-to-end connections by reserving the communication resources for addressing real-time requirements, and cope with network dynamicity (e.g., node/edge failures) in a distributed manner. According to our knowledge, this is the first distributed management scheme based on IEEE 802.15.4e standard, which guides the nodes in different phases from joining until publishing their sensor data in the network. We demonstrate via simulation that D-MSR can address real-time and reliable communication as well as the high throughput requirements of industrial automation wireless networks, while also achieving higher efficiency in network management than WirelessHART, in terms of delay and overhead. PMID:23807687

Coordinating Resource Usage through Adaptive Service Provisioning in Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Fok, Chien-Liang; Roman, Gruia-Catalin; Lu, Chenyang

Wireless sensor networks (WSNs) exhibit high levels of network dynamics and consist of devices with limited energy. This results in the need to coordinate applications not only at the functional level, as is traditionally done, but also in terms of resource utilization. In this paper, we present a middleware that does this using adaptive service provisioning. Novel service binding strategies automatically adapt application behavior when opportunities for energy savings surface, and switch providers when the network topology changes. The former is accomplished by providing limited information about the energy consumption associated with using various services, systematically exploiting opportunities for sharing service invocations, and exploiting the broadcast nature of wireless communication in WSNs. The middleware has been implemented and evaluated on two disparate WSN platforms, the TelosB and Imote2. Empirical results show that adaptive service provisioning can enable energy-aware service binding decisions that result in increased energy efficiency and significantly increase service availability, while imposing minimal additional burden on the application, service, and device developers. Two applications, medical patient monitoring and structural health monitoring, demonstrate the middleware's efficacy.

Method and system for determining induction motor speed

DOEpatents

Parlos, Alexander G.; Bharadwaj, Raj M.

2004-03-30

A non-linear, semi-parametric neural network-based adaptive filter is utilized to determine the dynamic speed of a rotating rotor within an induction motor, without the explicit use of a speed sensor, such as a tachometer, is disclosed. The neural network-based filter is developed using actual motor current measurements, voltage measurements, and nameplate information. The neural network-based adaptive filter is trained using an estimated speed calculator derived from the actual current and voltage measurements. The neural network-based adaptive filter uses voltage and current measurements to determine the instantaneous speed of a rotating rotor. The neural network-based adaptive filter also includes an on-line adaptation scheme that permits the filter to be readily adapted for new operating conditions during operations.

Engineering of Sensor Network Structure for Dependable Fusion

DTIC Science & Technology

2014-08-15

Lossy Wireless Sensor Networks , IEEE/ACM Transactions on Networking , (04 2013): 0. doi: 10.1109/TNET.2013.2256795 Soumik Sarkar, Kushal Mukherjee...Phoha, Bharat B. Madan, Asok Ray. Distributed Network Control for Mobile Multi-Modal Wireless Sensor Networks , Journal of Parallel and Distributed...Deadline Constraints, IEEE Transactions on Automatic Control special issue on Wireless Sensor and Actuator Networks , (01 2011): 1. doi: Eric Keller

Distributed sensor coordination for advanced energy systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tumer, Kagan

Motivation: The ability to collect key system level information is critical to the safe, efficient and reliable operation of advanced power systems. Recent advances in sensor technology have enabled some level of decision making directly at the sensor level. However, coordinating large numbers of sensors, particularly heterogeneous sensors, to achieve system level objectives such as predicting plant efficiency, reducing downtime or predicting outages requires sophisticated coordination algorithms. Indeed, a critical issue in such systems is how to ensure the interaction of a large number of heterogenous system components do not interfere with one another and lead to undesirable behavior. Objectivesmore » and Contributions: The long-term objective of this work is to provide sensor deployment, coordination and networking algorithms for large numbers of sensors to ensure the safe, reliable, and robust operation of advanced energy systems. Our two specific objectives are to: 1. Derive sensor performance metrics for heterogeneous sensor networks. 2. Demonstrate effectiveness, scalability and reconfigurability of heterogeneous sensor network in advanced power systems. The key technical contribution of this work is to push the coordination step to the design of the objective functions of the sensors, allowing networks of heterogeneous sensors to be controlled. By ensuring that the control and coordination is not specific to particular sensor hardware, this approach enables the design and operation of large heterogeneous sensor networks. In addition to the coordination coordination mechanism, this approach allows the system to be reconfigured in response to changing needs (e.g., sudden external events requiring new responses) or changing sensor network characteristics (e.g., sudden changes to plant condition). Impact: The impact of this work extends to a large class of problems relevant to the National Energy Technology Laboratory including sensor placement, heterogeneous sensor coordination, and sensor network control in advanced power systems. Each application has specific needs, but they all share the one crucial underlying problem: how to ensure that the interactions of a large number of heterogenous agents lead to coordinated system behavior. This proposal describes a new paradigm that addresses that very issue in a systematic way. Key Results and Findings: All milestones have been completed. Our results demonstrate that by properly shaping agent objective functions, we can develop large (up to 10,000 devices) heterogeneous sensor networks with key desirable properties. The first milestone shows that properly choosing agent-specific objective functions increases system performance by up to 99.9% compared to global evaluations. The second milestone shows evolutionary algorithms learn excellent sensor network coordination policies prior to network deployment, and these policies can be refined online once the network is deployed. The third milestone shows the resulting sensor networks networks are extremely robust to sensor noise, where networks with up to 25% sensor noise are capable of providing measurements with errors on the order of 10⁻³. The fourth milestone shows the resulting sensor networks are extremely robust to sensor failure, with 25% of the sensors in the system failing resulting in no significant performance losses after system reconfiguration.« less

A Multi-User Game-Theoretical Multipath Routing Protocol to Send Video-Warning Messages over Mobile Ad Hoc Networks.

PubMed

Mezher, Ahmad Mohamad; Igartua, Mónica Aguilar; de la Cruz Llopis, Luis J; Pallarès Segarra, Esteve; Tripp-Barba, Carolina; Urquiza-Aguiar, Luis; Forné, Jordi; Sanvicente Gargallo, Emilio

2015-04-17

The prevention of accidents is one of the most important goals of ad hoc networks in smart cities. When an accident happens, dynamic sensors (e.g., citizens with smart phones or tablets, smart vehicles and buses, etc.) could shoot a video clip of the accident and send it through the ad hoc network. With a video message, the level of seriousness of the accident could be much better evaluated by the authorities (e.g., health care units, police and ambulance drivers) rather than with just a simple text message. Besides, other citizens would be rapidly aware of the incident. In this way, smart dynamic sensors could participate in reporting a situation in the city using the ad hoc network so it would be possible to have a quick reaction warning citizens and emergency units. The deployment of an efficient routing protocol to manage video-warning messages in mobile Ad hoc Networks (MANETs) has important benefits by allowing a fast warning of the incident, which potentially can save lives. To contribute with this goal, we propose a multipath routing protocol to provide video-warning messages in MANETs using a novel game-theoretical approach. As a base for our work, we start from our previous work, where a 2-players game-theoretical routing protocol was proposed to provide video-streaming services over MANETs. In this article, we further generalize the analysis made for a general number of N players in the MANET. Simulations have been carried out to show the benefits of our proposal, taking into account the mobility of the nodes and the presence of interfering traffic. Finally, we also have tested our approach in a vehicular ad hoc network as an incipient start point to develop a novel proposal specifically designed for VANETs.

Citizen sensors for SHM: use of accelerometer data from smartphones.

PubMed

Feng, Maria; Fukuda, Yoshio; Mizuta, Masato; Ozer, Ekin

2015-01-29

Ubiquitous smartphones have created a significant opportunity to form a low-cost wireless Citizen Sensor network and produce big data for monitoring structural integrity and safety under operational and extreme loads. Such data are particularly useful for rapid assessment of structural damage in a large urban setting after a major event such as an earthquake. This study explores the utilization of smartphone accelerometers for measuring structural vibration, from which structural health and post-event damage can be diagnosed. Widely available smartphones are tested under sinusoidal wave excitations with frequencies in the range relevant to civil engineering structures. Large-scale seismic shaking table tests, observing input ground motion and response of a structural model, are carried out to evaluate the accuracy of smartphone accelerometers under operational, white-noise and earthquake excitations of different intensity. Finally, the smartphone accelerometers are tested on a dynamically loaded bridge. The extensive experiments show satisfactory agreements between the reference and smartphone sensor measurements in both time and frequency domains, demonstrating the capability of the smartphone sensors to measure structural responses ranging from low-amplitude ambient vibration to high-amplitude seismic response. Encouraged by the results of this study, the authors are developing a citizen-engaging and data-analytics crowdsourcing platform towards a smartphone-based Citizen Sensor network for structural health monitoring and post-event damage assessment applications.

Effect of retransmission and retrodiction on estimation and fusion in long-haul sensor networks

DOE PAGES

Liu, Qiang; Wang, Xin; Rao, Nageswara S. V.; ...

2016-01-01

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as target tracking. In this work, we study the scenario where sensors take measurements of one or more dynamic targets and send state estimates of the targets to a fusion center via satellite links. The severe loss and delay inherent over the satellite channels reduce the number of estimates successfully arriving at the fusion center, thereby limiting the potential fusion gain and resulting in suboptimal accuracy performance of the fused estimates. In addition, the errors in target-sensor data association can alsomore » degrade the estimation performance. To mitigate the effect of imperfect communications on state estimation and fusion, we consider retransmission and retrodiction. The system adopts certain retransmission-based transport protocols so that lost messages can be recovered over time. Besides, retrodiction/smoothing techniques are applied so that the chances of incurring excess delay due to retransmission are greatly reduced. We analyze the extent to which retransmission and retrodiction can improve the performance of delay-sensitive target tracking tasks under variable communication loss and delay conditions. Lastly, simulation results of a ballistic target tracking application are shown in the end to demonstrate the validity of our analysis.« less

An extreme events laboratory to provide network centric collaborative situation assessment and decision making

NASA Astrophysics Data System (ADS)

Panulla, Brian J.; More, Loretta D.; Shumaker, Wade R.; Jones, Michael D.; Hooper, Robert; Vernon, Jeffrey M.; Aungst, Stanley G.

2009-05-01

Rapid improvements in communications infrastructure and sophistication of commercial hand-held devices provide a major new source of information for assessing extreme situations such as environmental crises. In particular, ad hoc collections of humans can act as "soft sensors" to augment data collected by traditional sensors in a net-centric environment (in effect, "crowd-sourcing" observational data). A need exists to understand how to task such soft sensors, characterize their performance and fuse the data with traditional data sources. In order to quantitatively study such situations, as well as study distributed decision-making, we have developed an Extreme Events Laboratory (EEL) at The Pennsylvania State University. This facility provides a network-centric, collaborative situation assessment and decision-making capability by supporting experiments involving human observers, distributed decision making and cognition, and crisis management. The EEL spans the information chain from energy detection via sensors, human observations, signal and image processing, pattern recognition, statistical estimation, multi-sensor data fusion, visualization and analytics, and modeling and simulation. The EEL command center combines COTS and custom collaboration tools in innovative ways, providing capabilities such as geo-spatial visualization and dynamic mash-ups of multiple data sources. This paper describes the EEL and several on-going human-in-the-loop experiments aimed at understanding the new collective observation and analysis landscape.

Evaluation of a Prototype pCO2 Optical Sensor

NASA Astrophysics Data System (ADS)

Sanborn-Marsh, C.; Sutton, A.; Sabine, C. L.; Lawrence-Salvas, N.; Dietrich, C.

2016-12-01

Anthropogenic greenhouse gas emissions continue to rise, driving climate change and altering the ocean carbonate systems. Carbonate chemistry can be characterized by any two of the four parameters: pH, total alkalinity, dissolved inorganic carbon, and partial pressure of dissolved carbon dioxide gas (pCO2). To fully monitor these dynamic systems, researchers must deploy a more temporally and spatially comprehensive sensor network. Logistical challenges, such as the energy consumption, size, lifetime, depth range, and cost of pCO2 sensors have limited the network's reach so far. NOAA's Pacific Marine Environmental Laboratory has conducted assessment tests of a pCO2 optical sensor (optode), recently developed by Atamanchuk et al (2014). We hope to deploy this optode in the summer of 2017 on high-resolution moored profiler, along with temperature, salinity, and oxygen sensors. While most pCO2 optodes have energy consumptions of 3-10 W, this 36mm-diameter by 86mm-long instrument consumes a mere 7-80 mW. Initial testing showed that its accuracy varied within an absolute range of 2-75 μatm, depending on environmental conditions, including temperature, salinity, response time, and initial calibration. Further research independently examining the effects of each variable on the accuracy of the data will also be presented.

Distributed policy based access to networked heterogeneous ISR data sources

NASA Astrophysics Data System (ADS)

Bent, G.; Vyvyan, D.; Wood, David; Zerfos, Petros; Calo, Seraphin

2010-04-01

Within a coalition environment, ad hoc Communities of Interest (CoI's) come together, perhaps for only a short time, with different sensors, sensor platforms, data fusion elements, and networks to conduct a task (or set of tasks) with different coalition members taking different roles. In such a coalition, each organization will have its own inherent restrictions on how it will interact with the others. These are usually stated as a set of policies, including security and privacy policies. The capability that we want to enable for a coalition operation is to provide access to information from any coalition partner in conformance with the policies of all. One of the challenges in supporting such ad-hoc coalition operations is that of providing efficient access to distributed sources of data, where the applications requiring the data do not have knowledge of the location of the data within the network. To address this challenge the International Technology Alliance (ITA) program has been developing the concept of a Dynamic Distributed Federated Database (DDFD), also know as a Gaian Database. This type of database provides a means for accessing data across a network of distributed heterogeneous data sources where access to the information is controlled by a mixture of local and global policies. We describe how a network of disparate ISR elements can be expressed as a DDFD and how this approach enables sensor and other information sources to be discovered autonomously or semi-autonomously and/or combined, fused formally defined local and global policies.

A feedback-based secure path approach for wireless sensor network data collection.

PubMed

Mao, Yuxin; Wei, Guiyi

2010-01-01

The unattended nature of wireless sensor networks makes them very vulnerable to malicious attacks. Therefore, how to preserve secure data collection is an important issue to wireless sensor networks. In this paper, we propose a novel approach of secure data collection for wireless sensor networks. We explore secret sharing and multipath routing to achieve secure data collection in wireless sensor network with compromised nodes. We present a novel tracing-feedback mechanism, which makes full use of the routing functionality of wireless sensor networks, to improve the quality of data collection. The major advantage of the approach is that the secure paths are constructed as a by-product of data collection. The process of secure routing causes little overhead to the sensor nodes in the network. Compared with existing works, the algorithms of the proposed approach are easy to implement and execute in resource-constrained wireless sensor networks. According to the result of a simulation experiment, the performance of the approach is better than the recent approaches with a similar purpose.

Adaptive critics for dynamic optimization.

PubMed

Kulkarni, Raghavendra V; Venayagamoorthy, Ganesh Kumar

2010-06-01

A novel action-dependent adaptive critic design (ACD) is developed for dynamic optimization. The proposed combination of a particle swarm optimization-based actor and a neural network critic is demonstrated through dynamic sleep scheduling of wireless sensor motes for wildlife monitoring. The objective of the sleep scheduler is to dynamically adapt the sleep duration to node's battery capacity and movement pattern of animals in its environment in order to obtain snapshots of the animal on its trajectory uniformly. Simulation results show that the sleep time of the node determined by the actor critic yields superior quality of sensory data acquisition and enhanced node longevity. Copyright 2010 Elsevier Ltd. All rights reserved.

Applying Sensor-Based Technology to Improve Construction Safety Management.

PubMed

Zhang, Mingyuan; Cao, Tianzhuo; Zhao, Xuefeng

2017-08-11

Construction sites are dynamic and complicated systems. The movement and interaction of people, goods and energy make construction safety management extremely difficult. Due to the ever-increasing amount of information, traditional construction safety management has operated under difficult circumstances. As an effective way to collect, identify and process information, sensor-based technology is deemed to provide new generation of methods for advancing construction safety management. It makes the real-time construction safety management with high efficiency and accuracy a reality and provides a solid foundation for facilitating its modernization, and informatization. Nowadays, various sensor-based technologies have been adopted for construction safety management, including locating sensor-based technology, vision-based sensing and wireless sensor networks. This paper provides a systematic and comprehensive review of previous studies in this field to acknowledge useful findings, identify the research gaps and point out future research directions.

Applying Sensor-Based Technology to Improve Construction Safety Management

PubMed Central

Zhang, Mingyuan; Cao, Tianzhuo; Zhao, Xuefeng

2017-01-01

Construction sites are dynamic and complicated systems. The movement and interaction of people, goods and energy make construction safety management extremely difficult. Due to the ever-increasing amount of information, traditional construction safety management has operated under difficult circumstances. As an effective way to collect, identify and process information, sensor-based technology is deemed to provide new generation of methods for advancing construction safety management. It makes the real-time construction safety management with high efficiency and accuracy a reality and provides a solid foundation for facilitating its modernization, and informatization. Nowadays, various sensor-based technologies have been adopted for construction safety management, including locating sensor-based technology, vision-based sensing and wireless sensor networks. This paper provides a systematic and comprehensive review of previous studies in this field to acknowledge useful findings, identify the research gaps and point out future research directions. PMID:28800061

Toward a Dynamically Reconfigurable Computing and Communication System for Small Spacecraft

NASA Technical Reports Server (NTRS)

Kifle, Muli; Andro, Monty; Tran, Quang K.; Fujikawa, Gene; Chu, Pong P.

2003-01-01

Future science missions will require the use of multiple spacecraft with multiple sensor nodes autonomously responding and adapting to a dynamically changing space environment. The acquisition of random scientific events will require rapidly changing network topologies, distributed processing power, and a dynamic resource management strategy. Optimum utilization and configuration of spacecraft communications and navigation resources will be critical in meeting the demand of these stringent mission requirements. There are two important trends to follow with respect to NASA's (National Aeronautics and Space Administration) future scientific missions: the use of multiple satellite systems and the development of an integrated space communications network. Reconfigurable computing and communication systems may enable versatile adaptation of a spacecraft system's resources by dynamic allocation of the processor hardware to perform new operations or to maintain functionality due to malfunctions or hardware faults. Advancements in FPGA (Field Programmable Gate Array) technology make it possible to incorporate major communication and network functionalities in FPGA chips and provide the basis for a dynamically reconfigurable communication system. Advantages of higher computation speeds and accuracy are envisioned with tremendous hardware flexibility to ensure maximum survivability of future science mission spacecraft. This paper discusses the requirements, enabling technologies, and challenges associated with dynamically reconfigurable space communications systems.

Gust prediction via artificial hair sensor array and neural network

NASA Astrophysics Data System (ADS)

Pankonien, Alexander M.; Thapa Magar, Kaman S.; Beblo, Richard V.; Reich, Gregory W.

2017-04-01

Gust Load Alleviation (GLA) is an important aspect of flight dynamics and control that reduces structural loadings and enhances ride quality. In conventional GLA systems, the structural response to aerodynamic excitation informs the control scheme. A phase lag, imposed by inertia, between the excitation and the measurement inherently limits the effectiveness of these systems. Hence, direct measurement of the aerodynamic loading can eliminate this lag, providing valuable information for effective GLA system design. Distributed arrays of Artificial Hair Sensors (AHS) are ideal for surface flow measurements that can be used to predict other necessary parameters such as aerodynamic forces, moments, and turbulence. In previous work, the spatially distributed surface flow velocities obtained from an array of artificial hair sensors using a Single-State (or feedforward) Neural Network were found to be effective in estimating the steady aerodynamic parameters such as air speed, angle of attack, lift and moment coefficient. This paper extends the investigation of the same configuration to unsteady force and moment estimation, which is important for active GLA control design. Implementing a Recurrent Neural Network that includes previous-timestep sensor information, the hair sensor array is shown to be capable of capturing gust disturbances with a wide range of periods, reducing predictive error in lift and moment by 68% and 52% respectively. The L2 norms of the first layer of the weight matrices were compared showing a 23% emphasis on prior versus current information. The Recurrent architecture also improves robustness, exhibiting only a 30% increase in predictive error when undertrained as compared to a 170% increase by the Single-State NN. This diverse, localized information can thus be directly implemented into a control scheme that alleviates the gusts without waiting for a structural response or requiring user-intensive sensor calibration.

Linking Simulation with Formal Verification and Modeling of Wireless Sensor Network in TLA+

NASA Astrophysics Data System (ADS)

Martyna, Jerzy

In this paper, we present the results of the simulation of a wireless sensor network based on the flooding technique and SPIN protocols. The wireless sensor network was specified and verified by means of the TLA+ specification language [1]. For a model of wireless sensor network built this way simulation was carried with the help of specially constructed software tools. The obtained results allow us to predict the behaviour of the wireless sensor network in various topologies and spatial densities. Visualization of the output data enable precise examination of some phenomenas in wireless sensor networks, such as a hidden terminal, etc.

Long term deployment of ambient CO2 sensors in the Atacama Desert using a wireless sensor network

NASA Astrophysics Data System (ADS)

Pitz, S.; Szlavecz, K. A.; Szalay, A.; Gupchup, J. A.; Carlson, D.; Xia, L.

2011-12-01

Carbon dioxide (CO2) is one of the most important greenhouse gases affecting climate. Since the 1950's, accurate measurements of atmospheric CO2 have become important to scientists and policymakers alike. While global CO2 concentrations are increasing, the value and rate of change of the concentrations differs from location to location. More long-term and widely dispersed measurements will assist in our understanding of the carbon cycle and its dynamics. We deployed two Viasala GMP222 CO2 sensors at the ACT telescope in the high Atacama Desert in Chile. The sensors were deployed for a total of 5 months. The data from the sensors was collected by a wireless sensor network and transmitted back to a basestation and uploaded to a server so it could be monitored remotely. Weather data was collected from a nearby station at another telescope. A decrease of approximately 50 ppm was observed over the deployment period. This may make be a true trend because the deployment period coincided with the southern hemisphere summer when plant uptake is high but the amplitude of change seems high. Earth systems models suggest that the annual fluctuation should only be around 10 ppm for this site. In addition a large daily fluctuation was observed, but with no plant activity or discernable organic matter nearby this seems unlikely to be an actual phenomenon. The current hypothesis is that there may be some problems with the sensor's Fabry-Perot inferometer at low temperatures, less than -10 degrees Celsius. Testing shows that an overestimation of the concentration may be occurring at low temperatures but warrants further investigation. If these issues could be resolved, using a stationary or mobile sensor could potentially be used in more places to collect more data about the dynamics of atmospheric CO2 concentrations due to its low power and low maintenance needs. Also there are many other telescope sites in the American Southwest and South American deserts that have infrastructure currently in place. These high, dry places could be good locations for terrestrial CO2 sampling due to the lack of vegetation and anthropogenic sources.

Adaptive measurements of urban runoff quality

NASA Astrophysics Data System (ADS)

Wong, Brandon P.; Kerkez, Branko

2016-11-01

An approach to adaptively measure runoff water quality dynamics is introduced, focusing specifically on characterizing the timing and magnitude of urban pollutographs. Rather than relying on a static schedule or flow-weighted sampling, which can miss important water quality dynamics if parameterized inadequately, novel Internet-enabled sensor nodes are used to autonomously adapt their measurement frequency to real-time weather forecasts and hydrologic conditions. This dynamic approach has the potential to significantly improve the use of constrained experimental resources, such as automated grab samplers, which continue to provide a strong alternative to sampling water quality dynamics when in situ sensors are not available. Compared to conventional flow-weighted or time-weighted sampling schemes, which rely on preset thresholds, a major benefit of the approach is the ability to dynamically adapt to features of an underlying hydrologic signal. A 28 km2 urban watershed was studied to characterize concentrations of total suspended solids (TSS) and total phosphorus. Water quality samples were autonomously triggered in response to features in the underlying hydrograph and real-time weather forecasts. The study watershed did not exhibit a strong first flush and intraevent concentration variability was driven by flow acceleration, wherein the largest loadings of TSS and total phosphorus corresponded with the steepest rising limbs of the storm hydrograph. The scalability of the proposed method is discussed in the context of larger sensor network deployments, as well the potential to improving control of urban water quality.

On Applicability of Network Coding Technique for 6LoWPAN-based Sensor Networks.

PubMed

Amanowicz, Marek; Krygier, Jaroslaw

2018-05-26

In this paper, the applicability of the network coding technique in 6LoWPAN-based sensor multihop networks is examined. The 6LoWPAN is one of the standards proposed for the Internet of Things architecture. Thus, we can expect the significant growth of traffic in such networks, which can lead to overload and decrease in the sensor network lifetime. The authors propose the inter-session network coding mechanism that can be implemented in resource-limited sensor motes. The solution reduces the overall traffic in the network, and in consequence, the energy consumption is decreased. Used procedures take into account deep header compressions of the native 6LoWPAN packets and the hop-by-hop changes of the header structure. Applied simplifications reduce signaling traffic that is typically occurring in network coding deployments, keeping the solution usefulness for the wireless sensor networks with limited resources. The authors validate the proposed procedures in terms of end-to-end packet delay, packet loss ratio, traffic in the air, total energy consumption, and network lifetime. The solution has been tested in a real wireless sensor network. The results confirm the efficiency of the proposed technique, mostly in delay-tolerant sensor networks.

Multi-objects recognition for distributed intelligent sensor networks

NASA Astrophysics Data System (ADS)

He, Haibo; Chen, Sheng; Cao, Yuan; Desai, Sachi; Hohil, Myron E.

2008-04-01

This paper proposes an innovative approach for multi-objects recognition for homeland security and defense based intelligent sensor networks. Unlike the conventional way of information analysis, data mining in such networks is typically characterized with high information ambiguity/uncertainty, data redundancy, high dimensionality and real-time constrains. Furthermore, since a typical military based network normally includes multiple mobile sensor platforms, ground forces, fortified tanks, combat flights, and other resources, it is critical to develop intelligent data mining approaches to fuse different information resources to understand dynamic environments, to support decision making processes, and finally to achieve the goals. This paper aims to address these issues with a focus on multi-objects recognition. Instead of classifying a single object as in the traditional image classification problems, the proposed method can automatically learn multiple objectives simultaneously. Image segmentation techniques are used to identify the interesting regions in the field, which correspond to multiple objects such as soldiers or tanks. Since different objects will come with different feature sizes, we propose a feature scaling method to represent each object in the same number of dimensions. This is achieved by linear/nonlinear scaling and sampling techniques. Finally, support vector machine (SVM) based learning algorithms are developed to learn and build the associations for different objects, and such knowledge will be adaptively accumulated for objects recognition in the testing stage. We test the effectiveness of proposed method in different simulated military environments.

A Novel Topology Control Approach to Maintain the Node Degree in Dynamic Wireless Sensor Networks

PubMed Central

Huang, Yuanjiang; Martínez, José-Fernán; Díaz, Vicente Hernández; Sendra, Juana

2014-01-01

Topology control is an important technique to improve the connectivity and the reliability of Wireless Sensor Networks (WSNs) by means of adjusting the communication range of wireless sensor nodes. In this paper, a novel Fuzzy-logic Topology Control (FTC) is proposed to achieve any desired average node degree by adaptively changing communication range, thus improving the network connectivity, which is the main target of FTC. FTC is a fully localized control algorithm, and does not rely on location information of neighbors. Instead of designing membership functions and if-then rules for fuzzy-logic controller, FTC is constructed from the training data set to facilitate the design process. FTC is proved to be accurate, stable and has short settling time. In order to compare it with other representative localized algorithms (NONE, FLSS, k-Neighbor and LTRT), FTC is evaluated through extensive simulations. The simulation results show that: firstly, similar to k-Neighbor algorithm, FTC is the best to achieve the desired average node degree as node density varies; secondly, FTC is comparable to FLSS and k-Neighbor in terms of energy-efficiency, but is better than LTRT and NONE; thirdly, FTC has the lowest average maximum communication range than other algorithms, which indicates that the most energy-consuming node in the network consumes the lowest power. PMID:24608008

High-fidelity simulation capability for virtual testing of seismic and acoustic sensors

NASA Astrophysics Data System (ADS)

Wilson, D. Keith; Moran, Mark L.; Ketcham, Stephen A.; Lacombe, James; Anderson, Thomas S.; Symons, Neill P.; Aldridge, David F.; Marlin, David H.; Collier, Sandra L.; Ostashev, Vladimir E.

2005-05-01

This paper describes development and application of a high-fidelity, seismic/acoustic simulation capability for battlefield sensors. The purpose is to provide simulated sensor data so realistic that they cannot be distinguished by experts from actual field data. This emerging capability provides rapid, low-cost trade studies of unattended ground sensor network configurations, data processing and fusion strategies, and signatures emitted by prototype vehicles. There are three essential components to the modeling: (1) detailed mechanical signature models for vehicles and walkers, (2) high-resolution characterization of the subsurface and atmospheric environments, and (3) state-of-the-art seismic/acoustic models for propagating moving-vehicle signatures through realistic, complex environments. With regard to the first of these components, dynamic models of wheeled and tracked vehicles have been developed to generate ground force inputs to seismic propagation models. Vehicle models range from simple, 2D representations to highly detailed, 3D representations of entire linked-track suspension systems. Similarly detailed models of acoustic emissions from vehicle engines are under development. The propagation calculations for both the seismics and acoustics are based on finite-difference, time-domain (FDTD) methodologies capable of handling complex environmental features such as heterogeneous geologies, urban structures, surface vegetation, and dynamic atmospheric turbulence. Any number of dynamic sources and virtual sensors may be incorporated into the FDTD model. The computational demands of 3D FDTD simulation over tactical distances require massively parallel computers. Several example calculations of seismic/acoustic wave propagation through complex atmospheric and terrain environments are shown.

Wireless Sensor Networks for Developmental and Flight Instrumentation

NASA Technical Reports Server (NTRS)

Alena, Richard; Figueroa, Fernando; Becker, Jeffrey; Foster, Mark; Wang, Ray; Gamudevelli, Suman; Studor, George

2011-01-01

Wireless sensor networks (WSN) based on the IEEE 802.15.4 Personal Area Network and ZigBee Pro 2007 standards are finding increasing use in home automation and smart energy markets providing a framework for interoperable software. The Wireless Connections in Space Project, funded by the NASA Engineering and Safety Center, is developing technology, metrics and requirements for next-generation spacecraft avionics incorporating wireless data transport. The team from Stennis Space Center and Mobitrum Corporation, working under a NASA SBIR grant, has developed techniques for embedding plug-and-play software into ZigBee WSN prototypes implementing the IEEE 1451 Transducer Electronic Datasheet (TEDS) standard. The TEDS provides meta-information regarding sensors such as serial number, calibration curve and operational status. Incorporation of TEDS into wireless sensors leads directly to building application level software that can recognize sensors at run-time, dynamically instantiating sensors as they are added or removed. The Ames Research Center team has been experimenting with this technology building demonstration prototypes for on-board health monitoring. Innovations in technology, software and process can lead to dramatic improvements for managing sensor systems applied to Developmental and Flight Instrumentation (DFI) aboard aerospace vehicles. A brief overview of the plug-and-play ZigBee WSN technology is presented along with specific targets for application within the aerospace DFI market. The software architecture for the sensor nodes incorporating the TEDS information is described along with the functions of the Network Capable Gateway processor which bridges 802.15.4 PAN to the TCP/IP network. Client application software connects to the Gateway and is used to display TEDS information and real-time sensor data values updated every few seconds, incorporating error detection and logging to help measure performance and reliability in relevant target environments. Test results from our prototype WSN running the Mobitrum software system are summarized and the implications to the scalability and reliability for DFI applications are discussed. Our demonstration system, incorporating sensors for life support system and structural health monitoring is described along with test results obtained by running the demonstration prototype in relevant environments such as the Wireless Habitat Testbed at Johnson Space Center in Houston. An operations concept for improved sensor process flow from design to flight test is outlined specific to the areas of Environmental Control and Life Support System performance characterization and structural health monitoring of human-rated spacecraft. This operations concept will be used to highlight the areas where WSN technology, particularly plug-and-play software based on IEEE 1451, can improve the current process, resulting in significant reductions in the technical effort, overall cost and schedule for providing DFI capability for future spacecraft. RELEASED -

Multidirectional seismo-acoustic wavefield of strombolian explosions at Yasur, Vanuatu using a broadband seismo-acoustic network, infrasound arrays, and infrasonic sensors on tethered balloons

NASA Astrophysics Data System (ADS)

Matoza, R. S.; Jolly, A. D.; Fee, D.; Johnson, R.; Kilgour, G.; Christenson, B. W.; Garaebiti, E.; Iezzi, A. M.; Austin, A.; Kennedy, B.; Fitzgerald, R.; Key, N.

2016-12-01

Seismo-acoustic wavefields at volcanoes contain rich information on shallow magma transport and subaerial eruption processes. Acoustic wavefields from eruptions are predicted to be directional, but sampling this wavefield directivity is challenging because infrasound sensors are usually deployed on the ground surface. We attempt to overcome this observational limitation using a novel deployment of infrasound sensors on tethered balloons in tandem with a suite of dense ground-based seismo-acoustic, geochemical, and eruption imaging instrumentation. We present preliminary results from a field experiment at Yasur Volcano, Vanuatu from July 26th to August 4th 2016. Our observations include data from a temporary network of 11 broadband seismometers, 6 single infrasonic microphones, 7 small-aperture 3-element infrasound arrays, 2 infrasound sensor packages on tethered balloons, an FTIR, a FLIR, 2 scanning Flyspecs, and various visual imaging data. An introduction to the dataset and preliminary analysis of the 3D seismo-acoustic wavefield and source process will be presented. This unprecedented dataset should provide a unique window into processes operating in the shallow magma plumbing system and their relation to subaerial eruption dynamics.

Integrated monitoring of wind plant systems

NASA Astrophysics Data System (ADS)

Whelan, Matthew J.; Janoyan, Kerop D.; Qiu, Tong

2008-03-01

Wind power is a renewable source of energy that is quickly gaining acceptance by many. Advanced sensor technologies have currently focused solely on improving wind turbine rotor aerodynamics and increasing of the efficiency of the blade design and concentration. Alternatively, potential improvements in wind plant efficiency may be realized through reduction of reactionary losses of kinetic energy to the structural and substructural systems supporting the turbine mechanics. Investigation of the complete dynamic structural response of the wind plant is proposed using a large-scale, high-rate wireless sensor network. The wireless network enables sensors to be placed across the sizable structure, including the rotating blades, without consideration of cabling issues and the economic burden associated with large spools of measurement cables. A large array of multi-axis accelerometers is utilized to evaluate the modal properties of the system as well as individual members and would enable long-term structural condition monitoring of the wind turbine as well. Additionally, environmental parameters, including wind speed, temperature, and humidity, are wirelessly collected for correlation. Such a wireless system could be integrated with electrical monitoring sensors and actuators and incorporated into a remote multi-turbine centralized plant monitoring and control system.

Historical Building Monitoring Using an Energy-Efficient Scalable Wireless Sensor Network Architecture

PubMed Central

Capella, Juan V.; Perles, Angel; Bonastre, Alberto; Serrano, Juan J.

2011-01-01

We present a set of novel low power wireless sensor nodes designed for monitoring wooden masterpieces and historical buildings, in order to perform an early detection of pests. Although our previous star-based system configuration has been in operation for more than 13 years, it does not scale well for sensorization of large buildings or when deploying hundreds of nodes. In this paper we demonstrate the feasibility of a cluster-based dynamic-tree hierarchical Wireless Sensor Network (WSN) architecture where realistic assumptions of radio frequency data transmission are applied to cluster construction, and a mix of heterogeneous nodes are used to minimize economic cost of the whole system and maximize power saving of the leaf nodes. Simulation results show that the specialization of a fraction of the nodes by providing better antennas and some energy harvesting techniques can dramatically extend the life of the entire WSN and reduce the cost of the whole system. A demonstration of the proposed architecture with a new routing protocol and applied to termite pest detection has been implemented on a set of new nodes and should last for about 10 years, but it provides better scalability, reliability and deployment properties. PMID:22346630

Historical building monitoring using an energy-efficient scalable wireless sensor network architecture.

PubMed

Capella, Juan V; Perles, Angel; Bonastre, Alberto; Serrano, Juan J

2011-01-01

We present a set of novel low power wireless sensor nodes designed for monitoring wooden masterpieces and historical buildings, in order to perform an early detection of pests. Although our previous star-based system configuration has been in operation for more than 13 years, it does not scale well for sensorization of large buildings or when deploying hundreds of nodes. In this paper we demonstrate the feasibility of a cluster-based dynamic-tree hierarchical Wireless Sensor Network (WSN) architecture where realistic assumptions of radio frequency data transmission are applied to cluster construction, and a mix of heterogeneous nodes are used to minimize economic cost of the whole system and maximize power saving of the leaf nodes. Simulation results show that the specialization of a fraction of the nodes by providing better antennas and some energy harvesting techniques can dramatically extend the life of the entire WSN and reduce the cost of the whole system. A demonstration of the proposed architecture with a new routing protocol and applied to termite pest detection has been implemented on a set of new nodes and should last for about 10 years, but it provides better scalability, reliability and deployment properties.

Network Computing for Distributed Underwater Acoustic Sensors

DTIC Science & Technology

2014-03-31

underwater sensor network with mobility. In preparation. [3] EvoLogics (2013), Underwater Acoustic Modems, (Product Information Guide... Wireless Communications, 9(9), 2934–2944. [21] Pompili, D. and Akyildiz, I. (2010), A multimedia cross-layer protocol for underwater acoustic sensor networks ... Network Computing for Distributed Underwater Acoustic Sensors M. Barbeau E. Kranakis

Capacity Building for Research and Education in GIS/GPS Technology and Systems

DTIC Science & Technology

2015-05-20

In multi- sensor area Wireless Sensor Networking (WSN) fields will be explored. As a step forward the research to be conducted in WSN field is to...Agriculture Using Technology for Crops Scouting in Agriculture Application of Technology in Precision Agriculture Wireless Sensor Network (WSN) in...Cooperative Engagement Capability Range based algorithms for Wireless Sensor Network Self-configurable Wireless Sensor Network Energy Efficient Wireless

Using In-Situ Optical Sensors to Understand the Biogeochemistry of Dissolved Organic Matter Across a Stream Network

NASA Astrophysics Data System (ADS)

Wymore, Adam S.; Potter, Jody; Rodríguez-Cardona, Bianca; McDowell, William H.

2018-04-01

The advent of high-frequency in situ optical sensors provides new opportunities to study the biogeochemistry of dissolved organic matter (DOM) in aquatic ecosystems. We used fDOM (fluorescent dissolved organic matter) to examine the spatial and temporal variability in dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) across a heterogeneous stream network that varies in NO3- concentration. Across the ten study streams fDOM explained twice the variability in the concentration of DOC (r2 = 0.82) compared to DON (r2 = 0.39), which suggests that the N-rich fraction of DOM is either more variable in its sources or more bioreactive than the more stable C-rich fraction. Among sites, DON molar fluorescence was approximately 3x more variable than DOC molar fluorescence and was correlated with changes in inorganic N, indicating that DON is both more variable in composition as well as highly responsive to changes in inorganic N. Laboratory results also indicate that the fDOM sensors we used perform as well as the excitation-emission wavelength pair generally referred to as the "tryptophan-like" peak when measured under laboratory conditions. However, since neither the field sensor not the laboratory measurements explained a large percentage of variation in DON concentrations, challenges still remain for monitoring the ambient pool of dissolved organic nitrogen. Sensor networks provide new insights into the potential reactivity of DOM and the variability in DOC and DON biogeochemistry across sites. These insights are needed to build spatially explicit models describing organic matter dynamics and water quality.

Current-Sensitive Path Planning for an Underactuated Free-Floating Ocean Sensorweb

NASA Technical Reports Server (NTRS)

Dahl, Kristen P.; Thompson, David R.; McLaren, David; Chao, Yi; Chien, Steve

2011-01-01

This work investigates multi-agent path planning in strong, dynamic currents using thousands of highly under-actuated vehicles. We address the specific task of path planning for a global network of ocean-observing floats. These submersibles are typified by the Argo global network consisting of over 3000 sensor platforms. They can control their buoyancy to float at depth for data collection or rise to the surface for satellite communications. Currently, floats drift at a constant depth regardless of the local currents. However, accurate current forecasts have become available which present the possibility of intentionally controlling floats' motion by dynamically commanding them to linger at different depths. This project explores the use of these current predictions to direct float networks to some desired final formation or position. It presents multiple algorithms for such path optimization and demonstrates their advantage over the standard approach of constant-depth drifting.

Dynamic Data-Driven UAV Network for Plume Characterization

DTIC Science & Technology

2016-05-23

data collection where simulations and measurements become a symbiotic feedback control system where simulations inform measurement locations and the...and measurements become a symbiotic feedback control system where simulations inform measurement locations and the measured data augments simulations...data analysis techniques with mobile sensor data collection where simulations and measurements become a symbiotic feedback control system where

Cognitive LF-Ant: a novel protocol for healthcare wireless sensor networks.

PubMed

Sousa, Marcelo; Lopes, Waslon; Madeiro, Francisco; Alencar, Marcelo

2012-01-01

In this paper, the authors present the Cognitive LF-Ant protocol for emergency reporting in healthcare wireless sensor networks. The protocol is inspired by the natural behaviour of ants and a cognitive component provides the capabilities to dynamically allocate resources, in accordance with the emergency degree of each patient. The intra-cluster emergency reporting is inspired by the different capabilities of leg-manipulated ants. The inter-cluster reporting is aided by the cooperative modulation diversity with spectrum sensing, which can detect new emergency reporting requests and forward them. Simulations results show the decrease of average delay time as the probability of opportunistic access increases, which privileges the emergency reporting related to the patients with higher priority of resources' usage. Furthermore, the packet loss rate is decreased by the use of cooperative modulation diversity with spectrum sensing.

Cognitive LF-Ant: A Novel Protocol for Healthcare Wireless Sensor Networks

PubMed Central

Sousa, Marcelo; Lopes, Waslon; Madeiro, Francisco; Alencar, Marcelo

2012-01-01

In this paper, the authors present the Cognitive LF-Ant protocol for emergency reporting in healthcare wireless sensor networks. The protocol is inspired by the natural behaviour of ants and a cognitive component provides the capabilities to dynamically allocate resources, in accordance with the emergency degree of each patient. The intra-cluster emergency reporting is inspired by the different capabilities of leg-manipulated ants. The inter-cluster reporting is aided by the cooperative modulation diversity with spectrum sensing, which can detect new emergency reporting requests and forward them. Simulations results show the decrease of average delay time as the probability of opportunistic access increases, which privileges the emergency reporting related to the patients with higher priority of resources' usage. Furthermore, the packet loss rate is decreased by the use of cooperative modulation diversity with spectrum sensing. PMID:23112610

Sensor Webs as Virtual Data Systems for Earth Science

NASA Astrophysics Data System (ADS)

Moe, K. L.; Sherwood, R.

2008-05-01

The NASA Earth Science Technology Office established a 3-year Advanced Information Systems Technology (AIST) development program in late 2006 to explore the technical challenges associated with integrating sensors, sensor networks, data assimilation and modeling components into virtual data systems called "sensor webs". The AIST sensor web program was initiated in response to a renewed emphasis on the sensor web concepts. In 2004, NASA proposed an Earth science vision for a more robust Earth observing system, coupled with remote sensing data analysis tools and advances in Earth system models. The AIST program is conducting the research and developing components to explore the technology infrastructure that will enable the visionary goals. A working statement for a NASA Earth science sensor web vision is the following: On-demand sensing of a broad array of environmental and ecological phenomena across a wide range of spatial and temporal scales, from a heterogeneous suite of sensors both in-situ and in orbit. Sensor webs will be dynamically organized to collect data, extract information from it, accept input from other sensor / forecast / tasking systems, interact with the environment based on what they detect or are tasked to perform, and communicate observations and results in real time. The focus on sensor webs is to develop the technology and prototypes to demonstrate the evolving sensor web capabilities. There are 35 AIST projects ranging from 1 to 3 years in duration addressing various aspects of sensor webs involving space sensors such as Earth Observing-1, in situ sensor networks such as the southern California earthquake network, and various modeling and forecasting systems. Some of these projects build on proof-of-concept demonstrations of sensor web capabilities like the EO-1 rapid fire response initially implemented in 2003. Other projects simulate future sensor web configurations to evaluate the effectiveness of sensor-model interactions for producing improved science predictions. Still other projects are maturing technology to support autonomous operations, communications and system interoperability. This paper will highlight lessons learned by various projects during the first half of the AIST program. Several sensor web demonstrations have been implemented and resulting experience with evolving standards, such as the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) among others, will be featured. The role of sensor webs in support of the intergovernmental Group on Earth Observations' Global Earth Observation System of Systems (GEOSS) will also be discussed. The GEOSS vision is a distributed system of systems that builds on international components to supply observing and processing systems that are, in the whole, comprehensive, coordinated and sustained. Sensor web prototypes are under development to demonstrate how remote sensing satellite data, in situ sensor networks and decision support systems collaborate in applications of interest to GEO, such as flood monitoring. Furthermore, the international Committee on Earth Observation Satellites (CEOS) has stepped up to the challenge to provide the space-based systems component for GEOSS. CEOS has proposed "virtual constellations" to address emerging data gaps in environmental monitoring, avoid overlap among observing systems, and make maximum use of existing space and ground assets. Exploratory applications that support the objectives of virtual constellations will also be discussed as a future role for sensor webs.

Demonstration of a roving-host wireless sensor network for rapid assessment monitoring of structural health

NASA Astrophysics Data System (ADS)

Mascarenas, David D. L.; Flynn, Eric; Lin, Kaisen; Farinholt, Kevin; Park, Gyuhae; Gupta, Rajesh; Todd, Michael; Farrar, Charles

2008-03-01

A major challenge impeding the deployment of wireless sensor networks for structural health monitoring (SHM) is developing means to supply power to the sensor nodes in a cost-effective manner. In this work an initial test of a roving-host wireless sensor network was performed on a bridge near Truth or Consequences, NM in August of 2007. The roving-host wireless sensor network features a radio controlled helicopter responsible for wirelessly delivering energy to sensor nodes on an "as-needed" basis. In addition, the helicopter also serves as a central data repository and processing center for the information collected by the sensor network. The sensor nodes used on the bridge were developed for measuring the peak displacement of the bridge, as well as measuring the preload of some of the bolted joints in the bridge. These sensors and sensor nodes were specifically designed to be able to operate from energy supplied wirelessly from the helicopter. The ultimate goal of this research is to ease the requirement for battery power supplies in wireless sensor networks.

An Embedded Multi-Agent Systems Based Industrial Wireless Sensor Network

PubMed Central

Brennan, Robert W.

2017-01-01

With the emergence of cyber-physical systems, there has been a growing interest in network-connected devices. One of the key requirements of a cyber-physical device is the ability to sense its environment. Wireless sensor networks are a widely-accepted solution for this requirement. In this study, an embedded multi-agent systems-managed wireless sensor network is presented. A novel architecture is proposed, along with a novel wireless sensor network architecture. Active and passive wireless sensor node types are defined, along with their communication protocols, and two application-specific examples are presented. A series of three experiments is conducted to evaluate the performance of the agent-embedded wireless sensor network. PMID:28906452

An Embedded Multi-Agent Systems Based Industrial Wireless Sensor Network.

PubMed

Taboun, Mohammed S; Brennan, Robert W

2017-09-14

With the emergence of cyber-physical systems, there has been a growing interest in network-connected devices. One of the key requirements of a cyber-physical device is the ability to sense its environment. Wireless sensor networks are a widely-accepted solution for this requirement. In this study, an embedded multi-agent systems-managed wireless sensor network is presented. A novel architecture is proposed, along with a novel wireless sensor network architecture. Active and passive wireless sensor node types are defined, along with their communication protocols, and two application-specific examples are presented. A series of three experiments is conducted to evaluate the performance of the agent-embedded wireless sensor network.

Stochastic global identification of a bio-inspired self-sensing composite UAV wing via wind tunnel experiments

NASA Astrophysics Data System (ADS)

Kopsaftopoulos, Fotios; Nardari, Raphael; Li, Yu-Hung; Wang, Pengchuan; Chang, Fu-Kuo

2016-04-01

In this work, the system design, integration, and wind tunnel experimental evaluation are presented for a bioinspired self-sensing intelligent composite unmanned aerial vehicle (UAV) wing. A total of 148 micro-sensors, including piezoelectric, strain, and temperature sensors, in the form of stretchable sensor networks are embedded in the layup of a composite wing in order to enable its self-sensing capabilities. Novel stochastic system identification techniques based on time series models and statistical parameter estimation are employed in order to accurately interpret the sensing data and extract real-time information on the coupled air flow-structural dynamics. Special emphasis is given to the wind tunnel experimental assessment under various flight conditions defined by multiple airspeeds and angles of attack. A novel modeling approach based on the recently introduced Vector-dependent Functionally Pooled (VFP) model structure is employed for the stochastic identification of the "global" coupled airflow-structural dynamics of the wing and their correlation with dynamic utter and stall. The obtained results demonstrate the successful system-level integration and effectiveness of the stochastic identification approach, thus opening new perspectives for the state sensing and awareness capabilities of the next generation of "fly-by-fee" UAVs.

A Smart and Balanced Energy-Efficient Multihop Clustering Algorithm (Smart-BEEM) for MIMO IoT Systems in Future Networks.

PubMed

Xu, Lina; O'Hare, Gregory M P; Collier, Rem

2017-07-05

Wireless Sensor Networks (WSNs) are typically composed of thousands of sensors powered by limited energy resources. Clustering techniques were introduced to prolong network longevity offering the promise of green computing. However, most existing work fails to consider the network coverage when evaluating the lifetime of a network. We believe that balancing the energy consumption in per unit area rather than on each single sensor can provide better-balanced power usage throughout the network. Our former work-Balanced Energy-Efficiency (BEE) and its Multihop version BEEM can not only extend the network longevity, but also maintain the network coverage. Following WSNs, Internet of Things (IoT) technology has been proposed with higher degree of diversities in terms of communication abilities and user scenarios, supporting a large range of real world applications. The IoT devices are embedded with multiple communication interfaces, normally referred as Multiple-In and Multiple-Out (MIMO) in 5G networks. The applications running on those devices can generate various types of data. Every interface has its own characteristics, which may be preferred and beneficial in some specific user scenarios. With MIMO becoming more available on the IoT devices, an advanced clustering solution for highly dynamic IoT systems is missing and also pressingly demanded in order to cater for differing user applications. In this paper, we present a smart clustering algorithm (Smart-BEEM) based on our former work BEE(M) to accomplish energy efficient and Quality of user Experience (QoE) supported communication in cluster based IoT networks. It is a user behaviour and context aware approach, aiming to facilitate IoT devices to choose beneficial communication interfaces and cluster headers for data transmission. Experimental results have proved that Smart-BEEM can further improve the performance of BEE and BEEM for coverage sensitive longevity.

A Smart and Balanced Energy-Efficient Multihop Clustering Algorithm (Smart-BEEM) for MIMO IoT Systems in Future Networks †

PubMed Central

O’Hare, Gregory M. P.; Collier, Rem

2017-01-01

Wireless Sensor Networks (WSNs) are typically composed of thousands of sensors powered by limited energy resources. Clustering techniques were introduced to prolong network longevity offering the promise of green computing. However, most existing work fails to consider the network coverage when evaluating the lifetime of a network. We believe that balancing the energy consumption in per unit area rather than on each single sensor can provide better-balanced power usage throughout the network. Our former work—Balanced Energy-Efficiency (BEE) and its Multihop version BEEM can not only extend the network longevity, but also maintain the network coverage. Following WSNs, Internet of Things (IoT) technology has been proposed with higher degree of diversities in terms of communication abilities and user scenarios, supporting a large range of real world applications. The IoT devices are embedded with multiple communication interfaces, normally referred as Multiple-In and Multiple-Out (MIMO) in 5G networks. The applications running on those devices can generate various types of data. Every interface has its own characteristics, which may be preferred and beneficial in some specific user scenarios. With MIMO becoming more available on the IoT devices, an advanced clustering solution for highly dynamic IoT systems is missing and also pressingly demanded in order to cater for differing user applications. In this paper, we present a smart clustering algorithm (Smart-BEEM) based on our former work BEE(M) to accomplish energy efficient and Quality of user Experience (QoE) supported communication in cluster based IoT networks. It is a user behaviour and context aware approach, aiming to facilitate IoT devices to choose beneficial communication interfaces and cluster headers for data transmission. Experimental results have proved that Smart-BEEM can further improve the performance of BEE and BEEM for coverage sensitive longevity. PMID:28678164

Bio-Inspired Stretchable Absolute Pressure Sensor Network

PubMed Central

Guo, Yue; Li, Yu-Hung; Guo, Zhiqiang; Kim, Kyunglok; Chang, Fu-Kuo; Wang, Shan X.

2016-01-01

A bio-inspired absolute pressure sensor network has been developed. Absolute pressure sensors, distributed on multiple silicon islands, are connected as a network by stretchable polyimide wires. This sensor network, made on a 4’’ wafer, has 77 nodes and can be mounted on various curved surfaces to cover an area up to 0.64 m × 0.64 m, which is 100 times larger than its original size. Due to Micro Electro-Mechanical system (MEMS) surface micromachining technology, ultrathin sensing nodes can be realized with thicknesses of less than 100 µm. Additionally, good linearity and high sensitivity (~14 mV/V/bar) have been achieved. Since the MEMS sensor process has also been well integrated with a flexible polymer substrate process, the entire sensor network can be fabricated in a time-efficient and cost-effective manner. Moreover, an accurate pressure contour can be obtained from the sensor network. Therefore, this absolute pressure sensor network holds significant promise for smart vehicle applications, especially for unmanned aerial vehicles. PMID:26729134

A Modular Plug-And-Play Sensor System for Urban Air Pollution Monitoring: Design, Implementation and Evaluation.

PubMed

Yi, Wei-Ying; Leung, Kwong-Sak; Leung, Yee

2017-12-22

Urban air pollution has caused public concern globally because it seriously affects human life. Modern monitoring systems providing pollution information with high spatio-temporal resolution have been developed to identify personal exposures. However, these systems' hardware specifications and configurations are usually fixed according to the applications. They can be inconvenient to maintain, and difficult to reconfigure and expand with respect to sensing capabilities. This paper aims at tackling these issues by adopting the proposed Modular Sensor System (MSS) architecture and Universal Sensor Interface (USI), and modular design in a sensor node. A compact MSS sensor node is implemented and evaluated. It has expandable sensor modules with plug-and-play feature and supports multiple Wireless Sensor Networks (WSNs). Evaluation results show that MSS sensor nodes can easily fit in different scenarios, adapt to reconfigurations dynamically, and detect low concentration air pollution with high energy efficiency and good data accuracy. We anticipate that the efforts on system maintenance, adaptation, and evolution can be significantly reduced when deploying the system in the field.

A Modular Plug-And-Play Sensor System for Urban Air Pollution Monitoring: Design, Implementation and Evaluation

PubMed Central

2017-01-01

Urban air pollution has caused public concern globally because it seriously affects human life. Modern monitoring systems providing pollution information with high spatio-temporal resolution have been developed to identify personal exposures. However, these systems’ hardware specifications and configurations are usually fixed according to the applications. They can be inconvenient to maintain, and difficult to reconfigure and expand with respect to sensing capabilities. This paper aims at tackling these issues by adopting the proposed Modular Sensor System (MSS) architecture and Universal Sensor Interface (USI), and modular design in a sensor node. A compact MSS sensor node is implemented and evaluated. It has expandable sensor modules with plug-and-play feature and supports multiple Wireless Sensor Networks (WSNs). Evaluation results show that MSS sensor nodes can easily fit in different scenarios, adapt to reconfigurations dynamically, and detect low concentration air pollution with high energy efficiency and good data accuracy. We anticipate that the efforts on system maintenance, adaptation, and evolution can be significantly reduced when deploying the system in the field. PMID:29271952

Capacitive Pressure Sensor with High Sensitivity and Fast Response to Dynamic Interaction Based on Graphene and Porous Nylon Networks.

PubMed

He, Zhongfu; Chen, Wenjun; Liang, Binghao; Liu, Changyong; Yang, Leilei; Lu, Dongwei; Mo, Zichao; Zhu, Hai; Tang, Zikang; Gui, Xuchun

2018-04-18

Flexible pressure sensors are of great importance to be applied in artificial intelligence and wearable electronics. However, assembling a simple structure, high-performance capacitive pressure sensor, especially for monitoring the flow of liquids, is still a big challenge. Here, on the basis of a sandwich-like structure, we propose a facile capacitive pressure sensor optimized by a flexible, low-cost nylon netting, showing many merits including a high response sensitivity (0.33 kPa -1 ) in a low-pressure regime (<1 kPa), an ultralow detection limit as 3.3 Pa, excellent working stability after more than 1000 cycles, and synchronous monitoring for human pulses and clicks. More important, this sensor exhibits an ultrafast response speed (<20 ms), which enables its detection for the fast variations of a small applied pressure from the morphological changing processes of a droplet falling onto the sensor. Furthermore, a capacitive pressure sensor array is fabricated for demonstrating the ability to spatial pressure distribution. Our developed pressure sensors show great prospects in practical applications such as health monitoring, flexible tactile devices, and motion detection.

The Coverage Problem in Video-Based Wireless Sensor Networks: A Survey

PubMed Central

Costa, Daniel G.; Guedes, Luiz Affonso

2010-01-01

Wireless sensor networks typically consist of a great number of tiny low-cost electronic devices with limited sensing and computing capabilities which cooperatively communicate to collect some kind of information from an area of interest. When wireless nodes of such networks are equipped with a low-power camera, visual data can be retrieved, facilitating a new set of novel applications. The nature of video-based wireless sensor networks demands new algorithms and solutions, since traditional wireless sensor networks approaches are not feasible or even efficient for that specialized communication scenario. The coverage problem is a crucial issue of wireless sensor networks, requiring specific solutions when video-based sensors are employed. In this paper, it is surveyed the state of the art of this particular issue, regarding strategies, algorithms and general computational solutions. Open research areas are also discussed, envisaging promising investigation considering coverage in video-based wireless sensor networks. PMID:22163651

New-generation security network with synergistic IP sensors

NASA Astrophysics Data System (ADS)

Peshko, Igor

2007-09-01

Global Dynamic Monitoring and Security Network (GDMSN) for real-time monitoring of (1) environmental and atmospheric conditions: chemical, biological, radiological and nuclear hazards, climate/man-induced catastrophe areas and terrorism threats; (2) water, soil, food chain quantifiers, and public health care; (3) large government/public/ industrial/ military areas is proposed. Each GDMSN branch contains stationary or mobile terminals (ground, sea, air, or space manned/unmanned vehicles) equipped with portable sensors. The sensory data are transferred via telephone, Internet, TV, security camera and other wire/wireless or optical communication lines. Each sensor is a self-registering, self-reporting, plug-and-play, portable unit that uses unified electrical and/or optical connectors and operates with IP communication protocol. The variant of the system based just on optical technologies cannot be disabled by artificial high-power radio- or gamma-pulses or sunbursts. Each sensor, being supplied with a battery and monitoring means, can be used as a separate portable unit. Military personnel, police officers, firefighters, miners, rescue teams, and nuclear power plant personnel may individually use these sensors. Terminals may be supplied with sensors essential for that specific location. A miniature "universal" optical gas sensor for specific applications in life support and monitoring systems was designed and tested. The sensor is based on the physics of absorption and/or luminescence spectroscopy. It can operate at high pressures and elevated temperatures, such as in professional and military diving equipment, submarines, underground shelters, mines, command stations, aircraft, space shuttles, etc. To enable this capability, the multiple light emitters, detectors and data processing electronics are located within a specially protected chamber.

Distributed Estimation, Coding, and Scheduling in Wireless Visual Sensor Networks

ERIC Educational Resources Information Center

Yu, Chao

2013-01-01

In this thesis, we consider estimation, coding, and sensor scheduling for energy efficient operation of wireless visual sensor networks (VSN), which consist of battery-powered wireless sensors with sensing (imaging), computation, and communication capabilities. The competing requirements for applications of these wireless sensor networks (WSN)…

Nitrate dynamics within a stream-lake network through time and space

NASA Astrophysics Data System (ADS)

Loken, L. C.; Crawford, J. T.; Childress, E. S.; Casson, N. J.; Stanley, E. H.

2014-12-01

Nitrate dynamics in streams are governed by biology, hydrology, and geomorphology, and the ability to parse these drivers apart has improved with the development of accurate high-frequency sensors. By combining a stationary Eulerian and a quasi-Lagrangian sensor platform, we investigated the timing of nitrate flushing and identified locations of elevated biogeochemical cycling along a stream-lake network in Northern Wisconsin, USA. Two years of continuous oxygen, carbon dioxide, and discharge measurements were used to compute gross primary production (GPP) and ecosystem respiration (ER) downstream of a wetland reach of Allequash Creek. Metabolic rates and flow patterns were compared with nitrate concentrations measured every 30 minutes using an optical sensor. Additionally, we floated a sensor array from the headwater spring ponds through a heterogeneous stream reach consisting of wetlands, beaver ponds, forested segments, and two lakes. Two distinct temporal patterns of stream nitrate concentrations were observed. During high flow events such as spring snowmelt and summer rain events, nitrate concentrations increased from ~5 μM (baseflow) to 12 μM, suggesting flushing from catchment sources. During baseflow conditions, nitrate followed a diel cycle with a 0.3-1.0 μM daytime draw down. Daily nitrate reduction was positively correlated with GPP calculated from oxygen and carbon dioxide records. Lastly, spatial analyses revealed lowest nitrate concentrations in the wetland reach, approximately 2-3 μM lower than the upstream spring ponds, and downstream lakes and forested reaches. This snapshot implies greater nitrate removal potential in the wetland reach likely driven by denitrification in organic rich sediments and macrophyte uptake in the open canopy stream segment. Taken together the temporal and spatial results show the dynamics of hydrology, geomorphology, and biology to influence nitrate delivery and variability in ecosystem processing through a stream-lake system. Future ecosystem studies could benefit by including multiple reference frameworks to better assess processes not captured by a single station approach.

Optimizing Cluster Heads for Energy Efficiency in Large-Scale Heterogeneous Wireless Sensor Networks

DOE PAGES

Gu, Yi; Wu, Qishi; Rao, Nageswara S. V.

2010-01-01

Many complex sensor network applications require deploying a large number of inexpensive and small sensors in a vast geographical region to achieve quality through quantity. Hierarchical clustering is generally considered as an efficient and scalable way to facilitate the management and operation of such large-scale networks and minimize the total energy consumption for prolonged lifetime. Judicious selection of cluster heads for data integration and communication is critical to the success of applications based on hierarchical sensor networks organized as layered clusters. We investigate the problem of selecting sensor nodes in a predeployed sensor network to be the cluster heads tomore » minimize the total energy needed for data gathering. We rigorously derive an analytical formula to optimize the number of cluster heads in sensor networks under uniform node distribution, and propose a Distance-based Crowdedness Clustering algorithm to determine the cluster heads in sensor networks under general node distribution. The results from an extensive set of experiments on a large number of simulated sensor networks illustrate the performance superiority of the proposed solution over the clustering schemes based on k -means algorithm.« less

Wireless Sensor Network Applications for the Combat Air Forces

DTIC Science & Technology

2006-06-13

WIRELESS SENSOR NETWORK APPLICATIONS FOR THE COMBAT AIR FORCES GRADUATE RESEARCH PROJECT...Government. AFIT/IC4/ENG/06-05 WIRELESS SENSOR NETWORK APPLICATIONS FOR THE COMBAT AIR FORCES GRADUATE RESEARCH PROJECT Presented to the...Major, USAF June 2006 APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED AFIT/IC4/ENG/06-05 WIRELESS SENSOR NETWORK APPLICATIONS

A Feedback-Based Secure Path Approach for Wireless Sensor Network Data Collection

PubMed Central

Mao, Yuxin; Wei, Guiyi

2010-01-01

The unattended nature of wireless sensor networks makes them very vulnerable to malicious attacks. Therefore, how to preserve secure data collection is an important issue to wireless sensor networks. In this paper, we propose a novel approach of secure data collection for wireless sensor networks. We explore secret sharing and multipath routing to achieve secure data collection in wireless sensor network with compromised nodes. We present a novel tracing-feedback mechanism, which makes full use of the routing functionality of wireless sensor networks, to improve the quality of data collection. The major advantage of the approach is that the secure paths are constructed as a by-product of data collection. The process of secure routing causes little overhead to the sensor nodes in the network. Compared with existing works, the algorithms of the proposed approach are easy to implement and execute in resource-constrained wireless sensor networks. According to the result of a simulation experiment, the performance of the approach is better than the recent approaches with a similar purpose. PMID:22163424

Smart border: ad-hoc wireless sensor networks for border surveillance

NASA Astrophysics Data System (ADS)

He, Jun; Fallahi, Mahmoud; Norwood, Robert A.; Peyghambarian, Nasser

2011-06-01

Wireless sensor networks have been proposed as promising candidates to provide automated monitoring, target tracking, and intrusion detection for border surveillance. In this paper, we demonstrate an ad-hoc wireless sensor network system for border surveillance. The network consists of heterogeneously autonomous sensor nodes that distributively cooperate with each other to enable a smart border in remote areas. This paper also presents energy-aware and sleeping algorithms designed to maximize the operating lifetime of the deployed sensor network. Lessons learned in building the network and important findings from field experiments are shared in the paper.

Automated Construction of Node Software Using Attributes in a Ubiquitous Sensor Network Environment

PubMed Central

Lee, Woojin; Kim, Juil; Kang, JangMook

2010-01-01

In sensor networks, nodes must often operate in a demanding environment facing restrictions such as restricted computing resources, unreliable wireless communication and power shortages. Such factors make the development of ubiquitous sensor network (USN) applications challenging. To help developers construct a large amount of node software for sensor network applications easily and rapidly, this paper proposes an approach to the automated construction of node software for USN applications using attributes. In the proposed technique, application construction proceeds by first developing a model for the sensor network and then designing node software by setting the values of the predefined attributes. After that, the sensor network model and the design of node software are verified. The final source codes of the node software are automatically generated from the sensor network model. We illustrate the efficiency of the proposed technique by using a gas/light monitoring application through a case study of a Gas and Light Monitoring System based on the Nano-Qplus operating system. We evaluate the technique using a quantitative metric—the memory size of execution code for node software. Using the proposed approach, developers are able to easily construct sensor network applications and rapidly generate a large number of node softwares at a time in a ubiquitous sensor network environment. PMID:22163678

Automated construction of node software using attributes in a ubiquitous sensor network environment.

PubMed

Lee, Woojin; Kim, Juil; Kang, JangMook

2010-01-01

In sensor networks, nodes must often operate in a demanding environment facing restrictions such as restricted computing resources, unreliable wireless communication and power shortages. Such factors make the development of ubiquitous sensor network (USN) applications challenging. To help developers construct a large amount of node software for sensor network applications easily and rapidly, this paper proposes an approach to the automated construction of node software for USN applications using attributes. In the proposed technique, application construction proceeds by first developing a model for the sensor network and then designing node software by setting the values of the predefined attributes. After that, the sensor network model and the design of node software are verified. The final source codes of the node software are automatically generated from the sensor network model. We illustrate the efficiency of the proposed technique by using a gas/light monitoring application through a case study of a Gas and Light Monitoring System based on the Nano-Qplus operating system. We evaluate the technique using a quantitative metric-the memory size of execution code for node software. Using the proposed approach, developers are able to easily construct sensor network applications and rapidly generate a large number of node softwares at a time in a ubiquitous sensor network environment.

Geospace Science from Ground-based Magnetometer Arrays: Advances in Sensors, Data Collection, and Data Integration

NASA Astrophysics Data System (ADS)

Mann, Ian; Chi, Peter

2016-07-01

Networks of ground-based magnetometers now provide the basis for the diagnosis of magnetic disturbances associated with solar wind-magnetosphere-ionosphere coupling on a truly global scale. Advances in sensor and digitisation technologies offer increases in sensitivity in fluxgate, induction coil, and new micro-sensor technologies - including the promise of hybrid sensors. Similarly, advances in remote connectivity provide the capacity for truly real-time monitoring of global dynamics at cadences sufficient for monitoring and in many cases resolving system level spatio-temporal ambiguities especially in combination with conjugate satellite measurements. A wide variety of the plasmaphysical processes active in driving geospace dynamics can be monitored based on the response of the electrical current system, including those associated with changes in global convection, magnetospheric substorms and nightside tail flows, as well as due to solar wind changes in both dynamic pressure and in response to rotations of the direction of the IMF. Significantly, any changes to the dynamical system must be communicated by the propagation of long-period Alfven and/or compressional waves. These wave populations hence provide diagnostics for not only the energy transport by the wave fields themselves, but also provide a mechanism for diagnosing the structure of the background plasma medium through which the waves propagate. Ultra-low frequency (ULF) waves are especially significant in offering a monitor for mass density profiles, often invisible to particle detectors because of their very low energy, through the application of a variety of magneto-seismology and cross-phase techniques. Renewed scientific interest in the plasma waves associated with near-Earth substorm dynamics, including magnetosphere-ionosphere coupling at substorm onset and their relation to magnetotail flows, as well the importance of global scale ultra-low frequency waves for the energisation, transport, acceleration, and loss of electrons in the radiation belts promise high profile science returns. Integrated, global scale data products also have potential importance and application for real-time monitoring of the space weather threats to electrical power grids from geomagnetically induced currents. Such data exploitation increasingly relies on the collaborations between multiple national magnetometer arrays to generate single data products with common file format and data properties. We review advances in geospace science which can be delivered by networks of ground-based magnetometers - in terms of advances in sensors, data collection, and data integration - including through collaborations within the Ultra-Large Terrestrial International Magnetometer Array (ULTIMA) consortium.

Characterizing Intra-Urban Air Quality Gradients with a Spatially-Distributed Network

NASA Astrophysics Data System (ADS)

Zimmerman, N.; Ellis, A.; Schurman, M. I.; Gu, P.; Li, H.; Snell, L.; Gu, J.; Subramanian, R.; Robinson, A. L.; Apte, J.; Presto, A. A.

2016-12-01

City-wide air pollution measurements have typically relied on regulatory or research monitoring sites with low spatial density to assess population-scale exposure. However, air pollutant concentrations exhibit significant spatial variability depending on local sources and features of the built environment, which may not be well captured by the existing monitoring regime. To better understand urban spatial and temporal pollution gradients at 1 km resolution, a network of 12 real-time air quality monitoring stations was deployed beginning July 2016 in Pittsburgh, PA. The stations were deployed at sites along an urban-rural transect and in urban locations with a range of traffic, restaurant, and tall building densities to examine the impact of various modifiable factors. Measurements from the stationary monitoring stations were further supported by mobile monitoring, which provided higher spatial resolution pollutant measurements on nearby roadways and enabled routine calibration checks. The stationary monitoring measurements comprise ultrafine particle number (Aerosol Dynamics "MAGIC" CPC), PM2.5 (Met One Neighborhood PM Monitor), black carbon (Met One BC 1050), and a new low-cost air quality monitor, the Real-time Affordable Multi-Pollutant (RAMP) sensor package for measuring CO, NO2, SO2, O3, CO2, temperature and relative humidity. High time-resolution (sub-minute) measurements across the distributed monitoring network enable insight into dynamic pollutant behaviour. Our preliminary findings show that our instruments are sensitive to PM2.5 gradients exceeding 2 micro-grams per cubic meter and ultrafine particle gradients exceeding 1000 particles per cubic centimeter. Additionally, we have developed rigorous calibration protocols to characterize the RAMP sensor response and drift, as well as multiple linear regression models to convert sensor response into pollutant concentrations that are comparable to reference instrumentation.

IJA: an efficient algorithm for query processing in sensor networks.

PubMed

Lee, Hyun Chang; Lee, Young Jae; Lim, Ji Hyang; Kim, Dong Hwa

2011-01-01

One of main features in sensor networks is the function that processes real time state information after gathering needed data from many domains. The component technologies consisting of each node called a sensor node that are including physical sensors, processors, actuators and power have advanced significantly over the last decade. Thanks to the advanced technology, over time sensor networks have been adopted in an all-round industry sensing physical phenomenon. However, sensor nodes in sensor networks are considerably constrained because with their energy and memory resources they have a very limited ability to process any information compared to conventional computer systems. Thus query processing over the nodes should be constrained because of their limitations. Due to the problems, the join operations in sensor networks are typically processed in a distributed manner over a set of nodes and have been studied. By way of example while simple queries, such as select and aggregate queries, in sensor networks have been addressed in the literature, the processing of join queries in sensor networks remains to be investigated. Therefore, in this paper, we propose and describe an Incremental Join Algorithm (IJA) in Sensor Networks to reduce the overhead caused by moving a join pair to the final join node or to minimize the communication cost that is the main consumer of the battery when processing the distributed queries in sensor networks environments. At the same time, the simulation result shows that the proposed IJA algorithm significantly reduces the number of bytes to be moved to join nodes compared to the popular synopsis join algorithm.

IJA: An Efficient Algorithm for Query Processing in Sensor Networks

PubMed Central

Lee, Hyun Chang; Lee, Young Jae; Lim, Ji Hyang; Kim, Dong Hwa

2011-01-01

One of main features in sensor networks is the function that processes real time state information after gathering needed data from many domains. The component technologies consisting of each node called a sensor node that are including physical sensors, processors, actuators and power have advanced significantly over the last decade. Thanks to the advanced technology, over time sensor networks have been adopted in an all-round industry sensing physical phenomenon. However, sensor nodes in sensor networks are considerably constrained because with their energy and memory resources they have a very limited ability to process any information compared to conventional computer systems. Thus query processing over the nodes should be constrained because of their limitations. Due to the problems, the join operations in sensor networks are typically processed in a distributed manner over a set of nodes and have been studied. By way of example while simple queries, such as select and aggregate queries, in sensor networks have been addressed in the literature, the processing of join queries in sensor networks remains to be investigated. Therefore, in this paper, we propose and describe an Incremental Join Algorithm (IJA) in Sensor Networks to reduce the overhead caused by moving a join pair to the final join node or to minimize the communication cost that is the main consumer of the battery when processing the distributed queries in sensor networks environments. At the same time, the simulation result shows that the proposed IJA algorithm significantly reduces the number of bytes to be moved to join nodes compared to the popular synopsis join algorithm. PMID:22319375

Zone-Based Routing Protocol for Wireless Sensor Networks

PubMed Central

Venkateswarlu Kumaramangalam, Muni; Adiyapatham, Kandasamy; Kandasamy, Chandrasekaran

2014-01-01

Extensive research happening across the globe witnessed the importance of Wireless Sensor Network in the present day application world. In the recent past, various routing algorithms have been proposed to elevate WSN network lifetime. Clustering mechanism is highly successful in conserving energy resources for network activities and has become promising field for researches. However, the problem of unbalanced energy consumption is still open because the cluster head activities are tightly coupled with role and location of a particular node in the network. Several unequal clustering algorithms are proposed to solve this wireless sensor network multihop hot spot problem. Current unequal clustering mechanisms consider only intra- and intercluster communication cost. Proper organization of wireless sensor network into clusters enables efficient utilization of limited resources and enhances lifetime of deployed sensor nodes. This paper considers a novel network organization scheme, energy-efficient edge-based network partitioning scheme, to organize sensor nodes into clusters of equal size. Also, it proposes a cluster-based routing algorithm, called zone-based routing protocol (ZBRP), for elevating sensor network lifetime. Experimental results show that ZBRP out-performs interims of network lifetime and energy conservation with its uniform energy consumption among the cluster heads. PMID:27437455

Zone-Based Routing Protocol for Wireless Sensor Networks.

PubMed

Venkateswarlu Kumaramangalam, Muni; Adiyapatham, Kandasamy; Kandasamy, Chandrasekaran

2014-01-01

Extensive research happening across the globe witnessed the importance of Wireless Sensor Network in the present day application world. In the recent past, various routing algorithms have been proposed to elevate WSN network lifetime. Clustering mechanism is highly successful in conserving energy resources for network activities and has become promising field for researches. However, the problem of unbalanced energy consumption is still open because the cluster head activities are tightly coupled with role and location of a particular node in the network. Several unequal clustering algorithms are proposed to solve this wireless sensor network multihop hot spot problem. Current unequal clustering mechanisms consider only intra- and intercluster communication cost. Proper organization of wireless sensor network into clusters enables efficient utilization of limited resources and enhances lifetime of deployed sensor nodes. This paper considers a novel network organization scheme, energy-efficient edge-based network partitioning scheme, to organize sensor nodes into clusters of equal size. Also, it proposes a cluster-based routing algorithm, called zone-based routing protocol (ZBRP), for elevating sensor network lifetime. Experimental results show that ZBRP out-performs interims of network lifetime and energy conservation with its uniform energy consumption among the cluster heads.