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Proceedings Paper

Distributed sensor network control for power and bandwidth allocation in large sensor networks
Author(s): Ashit Talukder; A. Panangadan
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Paper Abstract

A variety of autonomous sensory platforms for earth and planetary science require distributed sensing and control capability to be able to operate in dynamic environments where the occurrence and frequency of events could be few and far-between. We discuss a novel control methodology for autonomous monitoring of space habitats and in-situ ground and ocean-based heterogeneous wireless distributed sensor networks. Such sensor networks need to have a lifetime of months or even years, while being effective at detecting and reporting events in real-time before they pose a danger. This requires an on-line resource manager or controller to economize and adaptively control all resources such as energy, communication bandwidth, and sensor sampling frequency. We present an event based control optimization formulation of the resource management problem for sensor networks and discuss a method to adaptively change desired system performance of the sensor network in response to events. This functionality is critical in field-deployable sensor networks where continuous operation is expensive and system adaptation is critical for extended operation in the face of dynamic external events. We show results on various synthetic heterogeneous sensor networks where only partially accurate information about the sensing system is available and illustrate the efficacy of the control algorithm in handling such incorrect models with a negligible increase in transmission of the optimal control settings. We show that the run-time performance of the control algorithm scales well with increasing number of sensors.

Paper Details

Date Published: 9 April 2007
PDF: 12 pages
Proc. SPIE 6574, Optical Pattern Recognition XVIII, 65740G (9 April 2007); doi: 10.1117/12.723637
Show Author Affiliations
Ashit Talukder, Univ. of Southern California (United States)
A. Panangadan, Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 6574:
Optical Pattern Recognition XVIII
David P. Casasent; Tien-Hsin Chao, Editor(s)

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