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Special Issue "Energy Efficiency and Intelligent Signal Processing for Wireless Sensing"

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (30 April 2008)

Special Issue Editor

Guest Editor
Prof. Dr. Xue Wang (Website)

9003 Building, Tsinghua Garden, Department of Precision Instrument, Tsinghua University, 100084 Beijing, China
Phone: 8610-62776161
Fax: +86 10 62776161
Interests: intelligent sensors; sensors networks; remote sensing; intelligent signal processing; Kalman filtering; multisensor fusion; intelligent maintenance and fault prognosis; measurement instrumentation; robust and optimal control

Special Issue Information

The objective of this special issue is to provide high quality research results on energy efficiency and intelligent signal processing for wireless sensing. Research articles with totally new results or comprehensive review are solicited which will provide a consolidated state-of-the-art in this areas. The full research, review, corresponding applications and high rated manuscripts addressing the following topics are encouraged for submission. There are no restrictions on the topics of interest of this special issue.

Keywords

  • energy and resource management
  • energy efficiency analysis
  • dynamic power management
  • distributed query processing
  • detection
  • classification
  • estimation
  • tracking
  • sensor tasking and control
  • in-network processing and aggregation
  • distributed control & actuation
  • distributed inference & fusion
  • fault tolerance
  • network coverage
  • connectivity & longevity
  • security & reliability
  • applications & demonstrations of wireless sensing
  • energy and mobility management algorithm
  • multi-sensor
  • multi-source information networking architectures
  • modeling
  • measurement and simulation of mobile networks
  • distributed and collaborative signal processing
  • integrated test-beds, experiments
  • algorithms and measurements
  • agent and intelligent computing
  • web and network sensing application
  • wireless & mobile computing
  • peer-to-peer technology
  • virtual sensing

Published Papers (17 papers)

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Research

Open AccessArticle Distributed Principal Component Analysis for Wireless Sensor Networks
Sensors 2008, 8(8), 4821-4850; doi:10.3390/s8084821
Received: 27 May 2008 / Revised: 29 July 2008 / Accepted: 4 July 2008 / Published: 11 August 2008
Cited by 27 | PDF Full-text (1046 KB) | HTML Full-text | XML Full-text
Abstract
The Principal Component Analysis (PCA) is a data dimensionality reduction technique well-suited for processing data from sensor networks. It can be applied to tasks like compression, event detection, and event recognition. This technique is based on a linear transform where the sensor [...] Read more.
The Principal Component Analysis (PCA) is a data dimensionality reduction technique well-suited for processing data from sensor networks. It can be applied to tasks like compression, event detection, and event recognition. This technique is based on a linear transform where the sensor measurements are projected on a set of principal components. When sensor measurements are correlated, a small set of principal components can explain most of the measurements variability. This allows to significantly decrease the amount of radio communication and of energy consumption. In this paper, we show that the power iteration method can be distributed in a sensor network in order to compute an approximation of the principal components. The proposed implementation relies on an aggregation service, which has recently been shown to provide a suitable framework for distributing the computation of a linear transform within a sensor network. We also extend this previous work by providing a detailed analysis of the computational, memory, and communication costs involved. A compression experiment involving real data validates the algorithm and illustrates the tradeoffs between accuracy and communication costs. Full article
Open AccessArticle An Energy-Efficient and High-Quality Video Transmission Architecture in Wireless Video-Based Sensor Networks
Sensors 2008, 8(8), 4529-4559; doi:10.3390/s8084529
Received: 15 March 2008 / Revised: 9 June 2008 / Accepted: 9 June 2008 / Published: 4 August 2008
Cited by 15 | PDF Full-text (2767 KB) | HTML Full-text | XML Full-text
Abstract
Technological progress in the fields of Micro Electro-Mechanical Systems (MEMS) and wireless communications and also the availability of CMOS cameras, microphones and small-scale array sensors, which may ubiquitously capture multimedia content from the field, have fostered the development of low-cost limited resources [...] Read more.
Technological progress in the fields of Micro Electro-Mechanical Systems (MEMS) and wireless communications and also the availability of CMOS cameras, microphones and small-scale array sensors, which may ubiquitously capture multimedia content from the field, have fostered the development of low-cost limited resources Wireless Video-based Sensor Networks (WVSN). With regards to the constraints of videobased sensor nodes and wireless sensor networks, a supporting video stream is not easy to implement with the present sensor network protocols. In this paper, a thorough architecture is presented for video transmission over WVSN called Energy-efficient and high-Quality Video transmission Architecture (EQV-Architecture). This architecture influences three layers of communication protocol stack and considers wireless video sensor nodes constraints like limited process and energy resources while video quality is preserved in the receiver side. Application, transport, and network layers are the layers in which the compression protocol, transport protocol, and routing protocol are proposed respectively, also a dropping scheme is presented in network layer. Simulation results over various environments with dissimilar conditions revealed the effectiveness of the architecture in improving the lifetime of the network as well as preserving the video quality. Full article
Open AccessArticle Localization Algorithm Based on a Spring Model (LASM) for Large Scale Wireless Sensor Networks
Sensors 2008, 8(3), 1797-1818; doi:10.3390/s8031797
Received: 6 December 2007 / Accepted: 12 March 2008 / Published: 15 March 2008
Cited by 9 | PDF Full-text (384 KB) | HTML Full-text | XML Full-text
Abstract
A navigation method for a lunar rover based on large scale wireless sensornetworks is proposed. To obtain high navigation accuracy and large exploration area, highnode localization accuracy and large network scale are required. However, thecomputational and communication complexity and time consumption are [...] Read more.
A navigation method for a lunar rover based on large scale wireless sensornetworks is proposed. To obtain high navigation accuracy and large exploration area, highnode localization accuracy and large network scale are required. However, thecomputational and communication complexity and time consumption are greatly increasedwith the increase of the network scales. A localization algorithm based on a spring model(LASM) method is proposed to reduce the computational complexity, while maintainingthe localization accuracy in large scale sensor networks. The algorithm simulates thedynamics of physical spring system to estimate the positions of nodes. The sensor nodesare set as particles with masses and connected with neighbor nodes by virtual springs. Thevirtual springs will force the particles move to the original positions, the node positionscorrespondingly, from the randomly set positions. Therefore, a blind node position can bedetermined from the LASM algorithm by calculating the related forces with the neighbornodes. The computational and communication complexity are O(1) for each node, since thenumber of the neighbor nodes does not increase proportionally with the network scale size.Three patches are proposed to avoid local optimization, kick out bad nodes and deal withnode variation. Simulation results show that the computational and communicationcomplexity are almost constant despite of the increase of the network scale size. The time consumption has also been proven to remain almost constant since the calculation steps arealmost unrelated with the network scale size. Full article
Open AccessArticle LQER: A Link Quality Estimation based Routing for Wireless Sensor Networks
Sensors 2008, 8(2), 1025-1038; doi:10.3390/s8021025
Received: 11 January 2008 / Accepted: 11 February 2008 / Published: 15 February 2008
Cited by 25 | PDF Full-text (601 KB) | HTML Full-text | XML Full-text
Abstract
Routing protocols are crucial to self-organize wireless sensor networks (WSNs),which have been widely studied in recent years. For some specific applications, both energyaware and reliable data transmission need to be considered together. Historical link statusshould be captured and taken into account in [...] Read more.
Routing protocols are crucial to self-organize wireless sensor networks (WSNs),which have been widely studied in recent years. For some specific applications, both energyaware and reliable data transmission need to be considered together. Historical link statusshould be captured and taken into account in making data forwarding decisions to achievethe data reliability and energy efficiency tradeoff. In this paper, a dynamic window concept(m, k) is presented to record the link historical information and a link quality estimation basedrouting protocol (LQER) are proposed, which integrates the approach of minimum hop fieldand (m, k). The performance of LQER is evaluated by extensive simulation experiments to bemore energy-aware, with lower loss rate and better scalability than MHFR [1] and MCR [2].Thus the WSNs with LQER get longer lifetime of networks and better link quality. Full article
Open AccessArticle Vertex Separators for Partitioning a Graph
Sensors 2008, 8(2), 635-657; doi:10.3390/s8020635
Received: 5 December 2007 / Accepted: 29 January 2008 / Published: 4 February 2008
Cited by 3 | PDF Full-text (496 KB) | HTML Full-text | XML Full-text
Abstract
Finite Element Method (FEM) is a well known technique extensively studiedfor spatial and temporal modeling of environmental processes, weather predictioncomputations, and intelligent signal processing for wireless sensors. The need for hugecomputational power arising in such applications to simulate physical phenomenoncorrectly mandates the [...] Read more.
Finite Element Method (FEM) is a well known technique extensively studiedfor spatial and temporal modeling of environmental processes, weather predictioncomputations, and intelligent signal processing for wireless sensors. The need for hugecomputational power arising in such applications to simulate physical phenomenoncorrectly mandates the use of massively parallel computers to distribute the workloadevenly. In this study, a novel heuristic algorithm called Line Graph Bisection whichpartitions a graph via vertex separators so as to balance the workload amongst theprocessors and to minimize the communication overhead is proposed. The proposedalgorithm is proved to be computationally feasible and makes cost-effective parallelimplementations possible to speed up the solution process. Full article
Open AccessArticle Novel Deployment Schemes for Mobile Sensor Networks
Sensors 2007, 7(11), 2907-2919; doi:10.3390/S7112907
Received: 8 November 2007 / Accepted: 19 November 2007 / Published: 21 November 2007
Cited by 49 | PDF Full-text (604 KB) | HTML Full-text | XML Full-text
Abstract
Virtual Force Algorithm (VFA) is becoming a main solution to area coverage forhomogeneous wireless sensor networks with random distribution of mobile sensor nodes.Consider the factors of the convergence, the boundary in Region Of Interest (ROI), effec-tive distance of acting force and useless [...] Read more.
Virtual Force Algorithm (VFA) is becoming a main solution to area coverage forhomogeneous wireless sensor networks with random distribution of mobile sensor nodes.Consider the factors of the convergence, the boundary in Region Of Interest (ROI), effec-tive distance of acting force and useless moving, etc, VFA is improved to overcome the aboveproblems. Furthermore, an expression of exponential function for the relationship of vir-tual force is proposed to converge rapidly. Extensive simulation results indicate that theseimproved VFA get better performance in coverage rate, moving energy consumption, conver-gence etc. than original VFA. Full article
Open AccessArticle Robust Forecasting for Energy Efficiency of Wireless Multimedia Sensor Networks
Sensors 2007, 7(11), 2779-2807; doi:10.3390/s7112779
Received: 25 October 2007 / Accepted: 14 November 2007 / Published: 15 November 2007
Cited by 10 | PDF Full-text (958 KB) | HTML Full-text | XML Full-text
Abstract
An important criterion of wireless sensor network is the energy efficiency inspecified applications. In this wireless multimedia sensor network, the observations arederived from acoustic sensors. Focused on the energy problem of target tracking, this paperproposes a robust forecasting method to enhance the [...] Read more.
An important criterion of wireless sensor network is the energy efficiency inspecified applications. In this wireless multimedia sensor network, the observations arederived from acoustic sensors. Focused on the energy problem of target tracking, this paperproposes a robust forecasting method to enhance the energy efficiency of wirelessmultimedia sensor networks. Target motion information is acquired by acoustic sensornodes while a distributed network with honeycomb configuration is constructed. Thereby,target localization is performed by multiple sensor nodes collaboratively through acousticsignal processing. A novel method, combining autoregressive moving average (ARMA)model and radial basis function networks (RBFNs), is exploited to perform robust targetposition forecasting during target tracking. Then sensor nodes around the target areawakened according to the forecasted target position. With committee decision of sensornodes, target localization is performed in a distributed manner and the uncertainty ofdetection is reduced. Moreover, a sensor-to-observer routing approach of the honeycombmesh network is investigated to solve the data reporting considering the residual energy ofsensor nodes. Target localization and forecasting are implemented in experiments.Meanwhile, sensor node awakening and dynamic routing are evaluated. Experimentalresults verify that energy efficiency of wireless multimedia sensor network is enhanced bythe proposed target tracking method. Full article
Open AccessArticle Hierarchical Wireless Multimedia Sensor Networks for Collaborative Hybrid Semi-Supervised Classifier Learning
Sensors 2007, 7(11), 2693-2722; doi:10.3390/s7112693
Received: 24 October 2007 / Accepted: 8 November 2007 / Published: 13 November 2007
Cited by 4 | PDF Full-text (2331 KB) | HTML Full-text | XML Full-text
Abstract
Wireless multimedia sensor networks (WMSN) have recently emerged as one ofthe most important technologies, driven by the powerful multimedia signal acquisition andprocessing abilities. Target classification is an important research issue addressed in WMSN,which has strict requirement in robustness, quickness and accuracy. This [...] Read more.
Wireless multimedia sensor networks (WMSN) have recently emerged as one ofthe most important technologies, driven by the powerful multimedia signal acquisition andprocessing abilities. Target classification is an important research issue addressed in WMSN,which has strict requirement in robustness, quickness and accuracy. This paper proposes acollaborative semi-supervised classifier learning algorithm to achieve durative onlinelearning for support vector machine (SVM) based robust target classification. The proposedalgorithm incrementally carries out the semi-supervised classifier learning process inhierarchical WMSN, with the collaboration of multiple sensor nodes in a hybrid computingparadigm. For decreasing the energy consumption and improving the performance, somemetrics are introduced to evaluate the effectiveness of the samples in specific sensor nodes,and a sensor node selection strategy is also proposed to reduce the impact of inevitablemissing detection and false detection. With the ant optimization routing, the learningprocess is implemented with the selected sensor nodes, which can decrease the energyconsumption. Experimental results demonstrate that the collaborative hybrid semi-supervised classifier learning algorithm can effectively implement target classification inhierarchical WMSN. It has outstanding performance in terms of energy efficiency and timecost, which verifies the effectiveness of the sensor nodes selection and ant optimizationrouting. Full article
Open AccessArticle Multi-agent Negotiation Mechanisms for Statistical Target Classification in Wireless Multimedia Sensor Networks
Sensors 2007, 7(10), 2201-2237; doi:10.3390/s7102201
Received: 24 September 2007 / Accepted: 9 October 2007 / Published: 11 October 2007
Cited by 2 | PDF Full-text (474 KB) | HTML Full-text | XML Full-text
Abstract
The recent availability of low cost and miniaturized hardware has allowedwireless sensor networks (WSNs) to retrieve audio and video data in real worldapplications, which has fostered the development of wireless multimedia sensor networks(WMSNs). Resource constraints and challenging multimedia data volume makedevelopment of [...] Read more.
The recent availability of low cost and miniaturized hardware has allowedwireless sensor networks (WSNs) to retrieve audio and video data in real worldapplications, which has fostered the development of wireless multimedia sensor networks(WMSNs). Resource constraints and challenging multimedia data volume makedevelopment of efficient algorithms to perform in-network processing of multimediacontents imperative. This paper proposes solving problems in the domain of WMSNs fromthe perspective of multi-agent systems. The multi-agent framework enables flexible networkconfiguration and efficient collaborative in-network processing. The focus is placed ontarget classification in WMSNs where audio information is retrieved by microphones. Todeal with the uncertainties related to audio information retrieval, the statistical approachesof power spectral density estimates, principal component analysis and Gaussian processclassification are employed. A multi-agent negotiation mechanism is specially developed toefficiently utilize limited resources and simultaneously enhance classification accuracy andreliability. The negotiation is composed of two phases, where an auction based approach isfirst exploited to allocate the classification task among the agents and then individual agentdecisions are combined by the committee decision mechanism. Simulation experiments withreal world data are conducted and the results show that the proposed statistical approachesand negotiation mechanism not only reduce memory and computation requi Full article
Open AccessArticle Wireless Sensor/Actuator Network Design for Mobile Control Applications
Sensors 2007, 7(10), 2157-2173; doi:10.3390/s7102157
Received: 3 October 2007 / Accepted: 8 October 2007 / Published: 9 October 2007
Cited by 75 | PDF Full-text (486 KB) | HTML Full-text | XML Full-text
Abstract
Wireless sensor/actuator networks (WSANs) are emerging as a new generationof sensor networks. Serving as the backbone of control applications, WSANs will enablean unprecedented degree of distributed and mobile control. However, the unreliability ofwireless communications and the real-time requirements of control applications raise [...] Read more.
Wireless sensor/actuator networks (WSANs) are emerging as a new generationof sensor networks. Serving as the backbone of control applications, WSANs will enablean unprecedented degree of distributed and mobile control. However, the unreliability ofwireless communications and the real-time requirements of control applications raise greatchallenges for WSAN design. With emphasis on the reliability issue, this paper presents anapplication-level design methodology for WSANs in mobile control applications. Thesolution is generic in that it is independent of the underlying platforms, environment,control system models, and controller design. To capture the link quality characteristics interms of packet loss rate, experiments are conducted on a real WSAN system. From theexperimental observations, a simple yet efficient method is proposed to deal withunpredictable packet loss on actuator nodes. Trace-based simulations give promisingresults, which demonstrate the effectiveness of the proposed approach. Full article
Open AccessArticle Time Series Forecasting Energy-efficient Organization of Wireless Sensor Networks
Sensors 2007, 7(9), 1766-1792; doi:10.3390/s7091766
Received: 9 August 2007 / Accepted: 4 September 2007 / Published: 5 September 2007
Cited by 12 | PDF Full-text (699 KB) | HTML Full-text | XML Full-text
Abstract
Due to their wide potential applications, wireless sensor networks have recentlyreceived tremendous attention. The strict energy constraints of sensor nodes result in thegreat challenges for energy efficiency. This paper investigates the energy efficiency problemand proposes an energy-efficient organization method with time series [...] Read more.
Due to their wide potential applications, wireless sensor networks have recentlyreceived tremendous attention. The strict energy constraints of sensor nodes result in thegreat challenges for energy efficiency. This paper investigates the energy efficiency problemand proposes an energy-efficient organization method with time series forecasting. Theorganization of wireless sensor networks is formulated for target tracking. Target model,multi-sensor model and energy model are defined accordingly. For the target trackingapplication, target localization is achieved by collaborative sensing with multi-sensor fusion.The historical localization results are utilized for adaptive target trajectory forecasting.Empirical mode decomposition is implemented to extract the inherent variation modes in thetime series of a target trajectory. Future target position is derived from autoregressivemoving average (ARMA) models, which forecast the decomposition components,respectively. Moreover, the energy-efficient organization method is presented to enhance theenergy efficiency of wireless sensor networks. The sensor nodes implement sensing tasksaccording to the probability awakening in a distributed manner. When the sensor nodestransfer their observations to achieve data fusion, the routing scheme is obtained by antcolony optimization. Thus, both the operation and communication energy consumption canbe minimized. Experimental results verify that the combination of the ARMA model andempirical mode decomposition can estimate the target position efficiently and energy savingis achieved by the proposed organization method in wireless sensor networks. Full article
Open AccessArticle Energy-efficient Optimization of Reorganization-Enabled Wireless Sensor Networks
Sensors 2007, 7(9), 1793-1816; doi:10.3390/s7091793
Received: 9 August 2007 / Accepted: 4 September 2007 / Published: 5 September 2007
Cited by 8 | PDF Full-text (826 KB) | HTML Full-text | XML Full-text
Abstract
This paper studies the target tracking problem in wireless sensor networkswhere sensor nodes are deployed randomly. To achieve tracking accuracy constrained byenergy consumption, an energy-efficient optimization approach that enablesreorganization of wireless sensor networks is proposed. The approach includes threephases which are related [...] Read more.
This paper studies the target tracking problem in wireless sensor networkswhere sensor nodes are deployed randomly. To achieve tracking accuracy constrained byenergy consumption, an energy-efficient optimization approach that enablesreorganization of wireless sensor networks is proposed. The approach includes threephases which are related to prediction, localization and recovery, respectively. A particlefilter algorithm is implemented on the sink node to forecast the future movement of thetarget in the first prediction phase. Upon the completion of this phase, the most energyefficient sensor nodes are awakened to collaboratively locate the target. Energy efficiencyis evaluated by the ratio of mutual information to energy consumption. The recoveryphase is needed to improve the robustness of the approach. It is performed when thetarget is missed because of the incorrect predicted target location. In order to recapture thetarget by awakening additional sensor nodes as few as possible, a genetic-algorithm-basedmechanism is introduced to cover the recovery area. We show that the proposed approachhas excellent tracking performance. Moreover, it can efficiently reduce energyconsumption, prolong network lifetime and reduce network overheads. Full article
Open AccessArticle Using LOTOS for FormalisingWireless Sensor Network Applications
Sensors 2007, 7(8), 1447-1461; doi:10.3390/s7081447
Received: 3 May 2007 / Accepted: 18 June 2007 / Published: 13 August 2007
Cited by 4 | PDF Full-text (248 KB) | HTML Full-text | XML Full-text
Abstract
The number of wireless sensor network (WSN) applications is rapidly increasingand becoming an integral part of sensor nodes. These applications have been widely devel-oped on TinyOS operating system using the nesC programming language. However, due tothe tight integration to physical world, limited [...] Read more.
The number of wireless sensor network (WSN) applications is rapidly increasingand becoming an integral part of sensor nodes. These applications have been widely devel-oped on TinyOS operating system using the nesC programming language. However, due tothe tight integration to physical world, limited node power and resources (CPU and memory)and complexity of combining components into an application, to build such applications isnot a trivial task. In this context, we present an approach for treating with this complexityadopting a formal description technique, namely LOTOS, for formalising the WSN applica-tions‘ behaviour. The formalisation has three main benefits: better understanding on how theapplication actually works, checking of desired properties of the application‘s behaviour, andsimulation facilities. In order to illustrate the proposed approach, we apply it to two nesCtraditional applications, namely BLink and Sense. Full article
Open AccessArticle Agent Collaborative Target Localization and Classification in Wireless Sensor Networks
Sensors 2007, 7(8), 1359-1386; doi:10.3390/s7081359
Received: 26 June 2007 / Accepted: 27 July 2007 / Published: 30 July 2007
Cited by 18 | PDF Full-text (557 KB) | HTML Full-text | XML Full-text
Abstract
Wireless sensor networks (WSNs) are autonomous networks that have beenfrequently deployed to collaboratively perform target localization and classification tasks.Their autonomous and collaborative features resemble the characteristics of agents. Suchsimilarities inspire the development of heterogeneous agent architecture for WSN in thispaper. The proposed [...] Read more.
Wireless sensor networks (WSNs) are autonomous networks that have beenfrequently deployed to collaboratively perform target localization and classification tasks.Their autonomous and collaborative features resemble the characteristics of agents. Suchsimilarities inspire the development of heterogeneous agent architecture for WSN in thispaper. The proposed agent architecture views WSN as multi-agent systems and mobileagents are employed to reduce in-network communication. According to the architecture,an energy based acoustic localization algorithm is proposed. In localization, estimate oftarget location is obtained by steepest descent search. The search algorithm adapts tomeasurement environments by dynamically adjusting its termination condition. With theagent architecture, target classification is accomplished by distributed support vectormachine (SVM). Mobile agents are employed for feature extraction and distributed SVMlearning to reduce communication load. Desirable learning performance is guaranteed bycombining support vectors and convex hull vectors. Fusion algorithms are designed tomerge SVM classification decisions made from various modalities. Real world experimentswith MICAz sensor nodes are conducted for vehicle localization and classification.Experimental results show the proposed agent architecture remarkably facilitates WSNdesigns and algorithm implementation. The localization and classification algorithms alsoprove to be accurate and energy efficient. Full article
Open AccessArticle Cluster-based Dynamic Energy Management for Collaborative Target Tracking in Wireless Sensor Networks
Sensors 2007, 7(7), 1193-1215; doi:10.3390/s7071193
Received: 26 June 2007 / Accepted: 12 July 2007 / Published: 13 July 2007
Cited by 31 | PDF Full-text (687 KB) | HTML Full-text | XML Full-text
Abstract
A primary criterion of wireless sensor network is energy efficiency. Focused onthe energy problem of target tracking in wireless sensor networks, this paper proposes acluster-based dynamic energy management mechanism. Target tracking problem isformulated by the multi-sensor detection model as well as energy [...] Read more.
A primary criterion of wireless sensor network is energy efficiency. Focused onthe energy problem of target tracking in wireless sensor networks, this paper proposes acluster-based dynamic energy management mechanism. Target tracking problem isformulated by the multi-sensor detection model as well as energy consumption model. Adistributed adaptive clustering approach is investigated to form a reasonable routingframework which has uniform cluster head distribution. Dijkstra’s algorithm is utilized toobtain optimal intra-cluster routing. Target position is predicted by particle filter. Thepredicted target position is adopted to estimate the idle interval of sensor nodes. Hence,dynamic awakening approach is exploited to prolong sleep time of sensor nodes so that theoperation energy consumption of wireless sensor network can be reduced. The sensornodes around the target wake up on time and act as sensing candidates. With the candidatesensor nodes and predicted target position, the optimal sensor node selection is considered.Binary particle swarm optimization is proposed to minimize the total energy consumptionduring collaborative sensing and data reporting. Experimental results verify that theproposed clustering approach establishes a low-energy communication structure while theenergy efficiency of wireless sensor networks is enhanced by cluster-based dynamic energymanagement. Full article
Open AccessArticle Distributed Peer-to-Peer Target Tracking in Wireless Sensor Networks
Sensors 2007, 7(6), 1001-1027; doi:10.3390/s7061001
Received: 6 June 2007 / Accepted: 25 June 2007 / Published: 25 June 2007
Cited by 23 | PDF Full-text (1376 KB) | HTML Full-text | XML Full-text
Abstract
Target tracking is usually a challenging application for wireless sensor networks(WSNs) because it is always computation-intensive and requires real-time processing. Thispaper proposes a practical target tracking system based on the auto regressive movingaverage (ARMA) model in a distributed peer-to-peer (P2P) signal processing [...] Read more.
Target tracking is usually a challenging application for wireless sensor networks(WSNs) because it is always computation-intensive and requires real-time processing. Thispaper proposes a practical target tracking system based on the auto regressive movingaverage (ARMA) model in a distributed peer-to-peer (P2P) signal processing framework.In the proposed framework, wireless sensor nodes act as peers that perform target detection,feature extraction, classification and tracking, whereas target localization requires thecollaboration between wireless sensor nodes for improving the accuracy and robustness.For carrying out target tracking under the constraints imposed by the limited capabilities ofthe wireless sensor nodes, some practically feasible algorithms, such as the ARMA modeland the 2-D integer lifting wavelet transform, are adopted in single wireless sensor nodesdue to their outstanding performance and light computational burden. Furthermore, aprogressive multi-view localization algorithm is proposed in distributed P2P signalprocessing framework considering the tradeoff between the accuracy and energyconsumption. Finally, a real world target tracking experiment is illustrated. Results fromexperimental implementations have demonstrated that the proposed target tracking systembased on a distributed P2P signal processing framework can make efficient use of scarceenergy and communication resources and achieve target tracking successfully. Full article
Open AccessArticle Distributed Particle Swarm Optimization and Simulated Annealing for Energy-efficient Coverage in Wireless Sensor Networks
Sensors 2007, 7(5), 628-648; doi:10.3390/s7050628
Received: 25 April 2007 / Accepted: 8 May 2007 / Published: 10 May 2007
Cited by 32 | PDF Full-text (403 KB) | HTML Full-text | XML Full-text
Abstract
The limited energy supply of wireless sensor networks poses a great challenge for the deployment of wireless sensor nodes. In this paper, we focus on energy-efficient coverage with distributed particle swarm optimization and simulated annealing. First, the energy-efficient coverage problem is formulated [...] Read more.
The limited energy supply of wireless sensor networks poses a great challenge for the deployment of wireless sensor nodes. In this paper, we focus on energy-efficient coverage with distributed particle swarm optimization and simulated annealing. First, the energy-efficient coverage problem is formulated with sensing coverage and energy consumption models. We consider the network composed of stationary and mobile nodes. Second, coverage and energy metrics are presented to evaluate the coverage rate and energy consumption of a wireless sensor network, where a grid exclusion algorithm extracts the coverage state and Dijkstra’s algorithm calculates the lowest cost path for communication. Then, a hybrid algorithm optimizes the energy consumption, in which particle swarm optimization and simulated annealing are combined to find the optimal deployment solution in a distributed manner. Simulated annealing is performed on multiple wireless sensor nodes, results of which are employed to correct the local and global best solution of particle swarm optimization. Simulations of wireless sensor node deployment verify that coverage performance can be guaranteed, energy consumption of communication is conserved after deployment optimization and the optimization performance is boosted by the distributed algorithm. Moreover, it is demonstrated that energy efficiency of wireless sensor networks is enhanced by the proposed optimization algorithm in target tracking applications. Full article

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