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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| Vydáno v: | IEEE transactions on signal processing Ročník 59; číslo 10; s. 4938 - 4953 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York, NY
IEEE
01.10.2011
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1053-587X, 1941-0476 |
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| Abstract | 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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| AbstractList | 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. We consider the problem of Sensor Selection for Event Detection in Wireless Sensor Networks (WSNs). We want to choose a subset of [Formula Omitted] out of [Formula Omitted] 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. |
| Author | Bajovic, D. Sinopoli, B. Xavier, J. |
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| Cites_doi | 10.1109/JSTSP.2007.914882 10.1016/j.patcog.2008.01.016 10.1007/11766247_40 10.1145/1102351.1102385 10.1016/j.laa.2005.10.046 10.1023/A:1021798932766 10.1109/79.985685 10.1090/S0002-9939-1961-0122827-1 10.1109/SPAWC.2010.5670891 10.1287/opre.43.4.684 10.1109/ACC.2009.5160423 10.1177/10943420020160030901 10.1109/IPSN.2006.244032 10.1007/978-94-015-9113-3 10.1109/TSP.2008.2007095 10.1016/S0020-0255(01)00089-5 10.1287/opre.14.4.699 10.1109/TSP.2009.2039823 |
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| Keywords | 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 |
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| References | ref13 ref14 ref11 ref10 ref1 nesterov (ref23) 1995 ref17 ref16 ref19 cover (ref18) 2006 bajovi (ref3) 2009 ref24 ref26 ref20 ref22 scharf (ref2) 1991 ref21 fedorov (ref15) 1972 ref8 ref7 bickson (ref12) 0 ref9 garey (ref25) 1979 bajovi (ref4) 2009 ref6 ref5 |
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| SubjectTerms | 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 |
| Title | Sensor Selection for Event Detection in Wireless Sensor Networks |
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