DocumentCode :
3255743
Title :
Bayesian quickest change point detection and localization in sensor networks
Author :
Jun Geng ; Lifeng Lai
Author_Institution :
Dept. of Electr. & Comput. Eng., Worcester Polytech. Inst., Worcester, MA, USA
fYear :
2013
fDate :
3-5 Dec. 2013
Firstpage :
871
Lastpage :
874
Abstract :
The problem of quickly detecting and locating a statistical change point using multiple sensors is considered. Specifically, a statistical change point occurs at a random time and at a random location in the coverage region (or the region of interest) of a multi-sensor network. Each sensor node in the network has a limited detection range hence the observations of each sensor would be affected by the change point only if the change point occurs in its vicinity. The region of interest is partitioned by the detection ranges of all the sensors in the network. One is required to detect the presence of the change and to locate the partition of the change based on the sequential observations from all sensor nodes. Three performance metrics, namely the average detection delay, the false alarm probability and the false location probability, are of interest. The goal is to find a stopping time τ, at which the decision maker stops taking more observations and claims the change has occurred, and a terminal decision rule δ, by which the decision maker locates the partition that the change occurs at, to minimize a weighted sum of these three performance metrics. We obtain the optimal solution by first converting the proposed problem to a Markovian stopping time problem and then solving the problem using the tools from the optimal stopping theory.
Keywords :
Bayes methods; Markov processes; wireless sensor networks; Bayesian quickest change point detection; Bayesian quickest change point localization; Markovian stopping time problem; average detection delay; false alarm probability; false location probability; multisensor network; optimal stopping theory; performance metrics; sensor networks; statistical change point detection; terminal decision rule; Bayes methods; Delays; Markov processes; Monitoring; Vectors; Bayesian change point detection; change point localization; sensor network; sequential detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE
Conference_Location :
Austin, TX
Type :
conf
DOI :
10.1109/GlobalSIP.2013.6737030
Filename :
6737030
Link To Document :
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