Transmission range control during autonomous node selection for wireless sensor networks

The operation of an effective wireless sensor network requires energy awareness. This paper focuses on the development of the autonomous node selection (ANS) algorithm as a means to conserve energy. The algorithm provides a protocol for a resource manager to conserve energy as a network of bearings-only sensors track a target in a decentralized manner. Specifically, each node within radio range of the active nodes uses the predicted target location to determine whether or not to actively measure and communicate bearing reports. The goal of ANS is to conserve energy by limiting communications while optimizing the geolocation performance of the tracker. Using derived bounds on the usefulness of nodes, the ANS method also conserves energy by limiting the transmission distance on the radios of the active nodes. As a result, nodes too far from the target to be useful can remain in a sleep mode. Once the target moves close enough to these nodes, they will receive transmissions from the active nodes and decide whether or not to actively sense and communicate. Simulations demonstrate the tradeoffs between geolocation performance and energy conservation when implementing different parameterizations of the ANS algorithm.

[1]  Feng Zhao,et al.  Scalable Information-Driven Sensor Querying and Routing for Ad Hoc Heterogeneous Sensor Networks , 2002, Int. J. High Perform. Comput. Appl..

[2]  Péter Molnár,et al.  Maximum likelihood methods for bearings-only target localization , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[3]  Randolph L. Moses,et al.  A Self-Localization Method for Wireless Sensor Networks , 2003, EURASIP J. Adv. Signal Process..

[4]  Wilson A turbulence spectral model for sound propagation in the atmosphere that incorporates shear and buoyancy forcings , 2000, The Journal of the Acoustical Society of America.

[5]  Michael O. Kolawole,et al.  Estimation and tracking , 2002 .

[6]  Yaakov Bar-Shalom,et al.  Estimation and Tracking: Principles, Techniques, and Software , 1993 .

[7]  Tien Pham,et al.  Adaptive wideband aeroacoustic array processing , 1996, Proceedings of 8th Workshop on Statistical Signal and Array Processing.

[8]  Alfonso Farina,et al.  Target tracking with bearings - Only measurements , 1999, Signal Process..

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

[10]  Mani Srivastava,et al.  Energy-aware wireless microsensor networks , 2002, IEEE Signal Process. Mag..

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

[12]  L. M. Kaplan Node selection for target tracking using bearing measurements from unattended ground sensors , 2003, 2003 IEEE Aerospace Conference Proceedings (Cat. No.03TH8652).

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

[14]  Jerry M. Mendel,et al.  Lessons in digital estimation theory , 1986 .

[15]  ZhaoFeng,et al.  Collaborative in-network processing for target tracking , 2003 .

[16]  H. F. Durrant-Whyte,et al.  Fully decentralised algorithm for multisensor Kalman filtering , 1991 .

[17]  Tien Pham,et al.  Simulation of detection and beamforming with acoustical ground sensors , 2002, SPIE Defense + Commercial Sensing.

[18]  Ivan Kadar Optimum geometry selection for sensor fusion , 1998, Defense, Security, and Sensing.

[19]  Ivan Kadar,et al.  Self-organizing cooperative sensor network for remote surveillance: target tracking while optimizing the geometry between bearing-reporting sensors and the target , 2001, SPIE Defense + Commercial Sensing.

[20]  Kung Yao,et al.  EURASIP Journal on Applied Signal Processing 2003:4, 359–370 c ○ 2003 Hindawi Publishing Corporation Acoustic Source Localization and Beamforming: Theory and Practice , 2002 .

[21]  Volkan Cevher,et al.  Sensor array calibration via tracking with the extended Kalman filter , 2001, SPIE Defense + Commercial Sensing.

[22]  Lance M. Kaplan,et al.  On exploiting propagation delays for passive target localization using bearings-only measurements , 2005, J. Frankl. Inst..