Sensor Selection for Event Detection in Wireless Sensor Networks

We consider the problem of sensor selection for event detection in wireless sensor networks (WSNs). We want to choose a subset of p out of n sensors that yields the best detection performance. As the sensor selection optimality criteria, we propose the Kullback-Leibler and Chernoff distances between...

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Vydané v:	IEEE transactions on signal processing Ročník 59; číslo 10; s. 4938 - 4953
Hlavní autori:	Bajovic, D., Sinopoli, B., Xavier, J.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.10.2011 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Applied sciences Chernoff distance Computer simulation Covariance matrix Criteria Detection, estimation, filtering, equalization, prediction Estimation Event detection Exact sciences and technology Heuristic Information, signal and communications theory Kullback-Leibler distance Networks Optimization Robot sensing systems robust optimization Robustness Searching sensor selection Sensors Signal and communications theory Signal, noise Studies Telecommunications and information theory Uncertainty Upper bounds Performance evaluation Chernoff distance sensor selection Wireless telecommunication robust optimization Algorithm Optimization Remote sensing Event analysis Random search Selection problem Computation time Upper bound Kullback―Leibler distance event detection Algorithm complexity NP hard problem Signal processing Wireless network Numerical simulation Sensor array Signal detection
ISSN:	1053-587X, 1941-0476
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Popis
Shrnutí:	We consider the problem of sensor selection for event detection in wireless sensor networks (WSNs). We want to choose a subset of p out of n sensors that yields the best detection performance. As the sensor selection optimality criteria, we propose the Kullback-Leibler and Chernoff distances between the distributions of the selected measurements under the two hypothesis. We formulate the maxmin robust sensor selection problem to cope with the uncertainties in distribution means. We prove that the sensor selection problem is NP hard, for both Kullback-Leibler and Chernoff criteria. To (sub)optimally solve the sensor selection problem, we propose an algorithm of affordable complexity. Extensive numerical simulations on moderate size problem instances (when the optimum by exhaustive search is feasible to compute) demonstrate the algorithm's near optimality in a very large portion of problem instances. For larger problems, extensive simulations demonstrate that our algorithm outperforms random searches, once an upper bound on computational time is set. We corroborate numerically the validity of the Kullback-Leibler and Chernoff sensor selection criteria, by showing that they lead to sensor selections nearly optimal both in the Neyman-Pearson and Bayes sense.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23
ISSN:	1053-587X 1941-0476
DOI:	10.1109/TSP.2011.2160630