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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Bibliographic Details
Published in:IEEE transactions on signal processing Vol. 59; no. 10; pp. 4938 - 4953
Main Authors: Bajovic, D., Sinopoli, B., Xavier, J.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.10.2011
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2011.2160630