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Distributed energy-efficient target tracking with binary sensor networks

Distributed energy-efficient target tracking with binary sensor networks Target tracking is a typical and important cooperative sensing application of wireless sensor networks. We study it in its most basic form, assuming a binary sensing model in which each sensor returns only 1-bit information regarding target's presence or absence within its sensing range. A novel, real-time and distributed target tracking algorithm is introduced. The algorithm is energy efficient and fault tolerant. It estimates the target location, velocity, and trajectory in a distributed and asynchronous manner. The accuracy of the algorithm is analytically derived under an ideal binary sensing model and extensive simulations of ideal, imperfect, and faulty sensing models show that the algorithm achieves good performance. It outperforms other published algorithms by yielding highly accurate estimates of the target's location, velocity, and trajectory. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Sensor Networks (TOSN) Association for Computing Machinery

Distributed energy-efficient target tracking with binary sensor networks

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Publisher
Association for Computing Machinery
Copyright
The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc.
Subject
Distributed applications
ISSN
1550-4859
DOI
10.1145/1777406.1777411
Publisher site
See Article on Publisher Site

Abstract

Target tracking is a typical and important cooperative sensing application of wireless sensor networks. We study it in its most basic form, assuming a binary sensing model in which each sensor returns only 1-bit information regarding target's presence or absence within its sensing range. A novel, real-time and distributed target tracking algorithm is introduced. The algorithm is energy efficient and fault tolerant. It estimates the target location, velocity, and trajectory in a distributed and asynchronous manner. The accuracy of the algorithm is analytically derived under an ideal binary sensing model and extensive simulations of ideal, imperfect, and faulty sensing models show that the algorithm achieves good performance. It outperforms other published algorithms by yielding highly accurate estimates of the target's location, velocity, and trajectory.

Journal

ACM Transactions on Sensor Networks (TOSN)Association for Computing Machinery

Published: Jul 1, 2010

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