Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Communication Networking: An Analytical Approach
Communication Networking: An Analytical Approach
Distributed detection and fusion in a large wireless sensor network of random size
EURASIP Journal on Wireless Communications and Networking
Distributed detection and localization of events in large ad hoc wireless sensor networks
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Wireless Networking
Anomaly detection in wireless sensor networks
IEEE Wireless Communications
Decentralized quickest change detection
IEEE Transactions on Information Theory
Efficient event prewarning for sensor networks with multi microenvironments
Euro-Par'13 Proceedings of the 19th international conference on Parallel Processing
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We consider a small extent sensor network for event detection, in which nodes periodically take samples and then contend over a random access network to transmit their measurement packets to the fusion center. We consider two procedures at the fusion center for processing the measurements. The Bayesian setting, is assumed, that is, the fusion center has a prior distribution on the change time. In the first procedure, the decision algorithm at the fusion center is network--oblivious and makes a decision only when a complete vector of measurements taken at a sampling instant is available. In the second procedure, the decision algorithm at the fusion center is network--aware and processes measurements as they arrive, but in a time-causal order. In this case, the decision statistic depends on the network delays, whereas in the network--oblivious case, the decision statistic does not. This yields a Bayesian change-detection problem with a trade-off between the random network delay and the decision delay that is, a higher sampling rate reduces the decision delay but increases the random access delay. Under periodic sampling, in the network--oblivious case, the structure of the optimal stopping rule is the same as that without the network, and the optimal change detection delay decouples into the network delay and the optimal decision delay without the network. In the network--aware case, the optimal stopping problem is analyzed as a partially observable Markov decision process, in which the states of the queues and delays in the network need to be maintained. A sufficient decision statistic is the network state and the posterior probability of change having occurred, given the measurements received and the state of the network. The optimal regimes are studied using simulation.