Vulnerability of Surveillance Networks to Faults

Tai-Lin Chin, Thomas Clouqueur, Parameswaran Ramanathan and Kewal K. Saluja
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Tai-Lin Chin: Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, USA
Thomas Clouqueur: Computer Design and Test Laboratory, Nara Institute of Science and Technology, Japan
Parameswaran Ramanathan: Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, USA
Kewal K. Saluja: Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, USA

International Journal of Distributed Sensor Networks, 2006, vol. 2, issue 3, 289-311

Abstract: Recent advances in technology have made it possible to build surveillance systems using many low-cost sensor nodes with limited computation and communication capabilities. Due to a potentially large number of nodes deployed, node failures are inevitable and can render a surveillance system that has degraded detection performances. The exposure metric has been proposed earlier to assess the quality of a surveillance system based on the detection performances. In this paper, we characterize the vulnerability of a system in terms of its exposure with respect to the number of faulty nodes and their combinations. Specifically, we assess the exposure of a surveillance network subject to a given number of faulty nodes, and identify the worst-case fault combination for both an idling target and a traversing target. For an idling target, the worst-case fault combination and exposure is analytically identified. For a traversing target, a genetic algorithm based approach is proposed to derive a near worst-case fault combination, and extensive simulation results are presented to show the effectiveness of the algorithm.

Keywords: Sensor Networks; Exposure; Detection; Sensor Deployment; Fault Tolerance (search for similar items in EconPapers)
Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:2:y:2006:i:3:p:289-311

DOI: 10.1080/15501320600692036

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