Virtual field strategy for collaborative signal and information processing in wireless heterogeneous sensor networks

A novel collaborative signal and information processing (CSIP) method, which is based on virtual fields excited by sensor nodes, is proposed for wireless heterogeneous sensor networks. These virtual fields influence states and operations in sensor nodes located in their regions of influence (ROIs) and thus collaboration is implemented through interactions between surrounding virtual fields and sensor nodes. Described by a group of radial basis functions (RBFs), virtual fields have different magnitudes and ROIs due to different initial energy, communication ranges, sensing ranges and information processing capabilities in heterogeneous sensor nodes. Dynamic mobile agent itinerary decision and adaptive node active probability updating are studied with virtual field strategies in a heterogeneous sensor network using mobile-agent-based computing paradigm. Simulation results demonstrate that this approach can reduce energy consumption in sensor nodes. Information gain efficiency and network lifetime are also increased.

[1]  David E. Culler,et al.  System architecture directions for networked sensors , 2000, SIGP.

[2]  R.L. Moses,et al.  Locating the nodes: cooperative localization in wireless sensor networks , 2005, IEEE Signal Processing Magazine.

[3]  Deborah Estrin,et al.  Cyclops: in situ image sensing and interpretation in wireless sensor networks , 2005, SenSys '05.

[4]  Xue Wang,et al.  Virtual Force-Directed Particle Swarm Optimization for Dynamic Deployment in Wireless Sensor Networks , 2007, ICIC.

[5]  Ramesh R. Rao,et al.  Improving energy saving in wireless systems by using dynamic power management , 2003, IEEE Trans. Wirel. Commun..

[6]  Xiaodong Wang,et al.  Joint multiple target tracking and classification in collaborative sensor networks , 2004, ISIT.

[7]  Deborah Estrin,et al.  Directed diffusion for wireless sensor networking , 2003, TNET.

[8]  S. Sitharama Iyengar,et al.  Multiresolution data integration using mobile agents in distributed sensor networks , 2001, IEEE Trans. Syst. Man Cybern. Part C.

[9]  Hairong Qi,et al.  Mobile agent migration modeling and design for target tracking in wireless sensor networks , 2008, Ad Hoc Networks.

[10]  Krishnendu Chakrabarty,et al.  Sensor deployment and target localization based on virtual forces , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[11]  Deborah Estrin,et al.  Medium access control with coordinated adaptive sleeping for wireless sensor networks , 2004, IEEE/ACM Transactions on Networking.

[12]  Anantha P. Chandrakasan,et al.  An application-specific protocol architecture for wireless microsensor networks , 2002, IEEE Trans. Wirel. Commun..

[13]  Feng Zhao,et al.  Information-Driven Dynamic Sensor Collaboration for Tracking Applications , 2002 .

[14]  Feng Zhao,et al.  Information-driven dynamic sensor collaboration , 2002, IEEE Signal Process. Mag..

[15]  M. Melamed.  Detection , 2021, SETI: Astronomy as a Contact Sport.

[16]  Jerry M. Mendel,et al.  Multi-category classification of ground vehicles based on the acoustic data of multiple terrains using fuzzy logic rule-based classifiers , 2005, SPIE Defense + Commercial Sensing.

[17]  Gregory J. Pottie,et al.  Wireless integrated network sensors , 2000, Commun. ACM.

[18]  Mohamed F. Younis,et al.  A survey on routing protocols for wireless sensor networks , 2005, Ad Hoc Networks.

[19]  Guilherme A. S. Pereira,et al.  A potential field approach for collecting data from sensor networks using mobile robots , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[20]  Qun Li,et al.  Navigation protocols in sensor networks , 2005, TOSN.

[21]  Matt Welsh,et al.  Sensor networks for emergency response: challenges and opportunities , 2004, IEEE Pervasive Computing.

[22]  Akbar M. Sayeed,et al.  Detection, classification, and tracking of targets - IEEE Signal Processing Magazine , 2001 .

[23]  Mario Fernando Montenegro Campos,et al.  Cooperative communication in ad hoc networked mobile robots , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[24]  Anna Scaglione,et al.  A scalable synchronization protocol for large scale sensor networks and its applications , 2005, IEEE Journal on Selected Areas in Communications.

[25]  Anantha Chandrakasan,et al.  Dynamic Power Management in Wireless Sensor Networks , 2001, IEEE Des. Test Comput..

[26]  Chin-Teng Lin,et al.  Tracking a maneuvering target using neural fuzzy network , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[27]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[28]  Yu Hen Hu,et al.  Detection, classification, and tracking of targets , 2002, IEEE Signal Process. Mag..

[29]  Hairong Qi,et al.  Mobile-agent-based collaborative signal and information processing in sensor networks , 2003, Proc. IEEE.

[30]  Chin-Teng Lin,et al.  Tracking a maneuvering target using neural fuzzy network , 2004, IEEE Trans. Syst. Man Cybern. Part B.

[31]  J. Elson,et al.  Fine-grained network time synchronization using reference broadcasts , 2002, OSDI '02.

[32]  Feng Zhao,et al.  Collaborative In-Network Processing for Target Tracking , 2003, EURASIP J. Adv. Signal Process..

[33]  Hamid Aghajan,et al.  Smart home care network using sensor fusion and distributed vision-based reasoning , 2006, VSSN '06.