IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Automatica (Journal of IFAC)
Asymptotic optimality of running consensus in testing binary hypotheses
IEEE Transactions on Signal Processing
Consensus-based Page's test in sensor networks
Signal Processing
Enforcing Consensus While Monitoring the Environment in Wireless Sensor Networks
IEEE Transactions on Signal Processing - Part II
Decentralized detection in sensor networks
IEEE Transactions on Signal Processing
Distributed Detection via Gaussian Running Consensus: Large Deviations Asymptotic Analysis
IEEE Transactions on Signal Processing
Distributed Change Detection Based on a Consensus Algorithm
IEEE Transactions on Signal Processing
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In this paper a novel distributed algorithm derived from the Generalized Likelihood Ratio is proposed for real time change detection using sensor networks. The algorithm is based on a combination of recursively generated local statistics and a global consensus strategy, and does not require any fusion center. The problem of detection of an unknown change in the mean of an observed random process is discussed and the performance of the algorithm is analyzed in the sense of a measure of the error with respect to the corresponding centralized algorithm. The analysis encompasses asymmetric constant and randomly time varying matrices describing communications in the network, as well as constant and time varying forgetting factors in the underlying recursions. An analogous algorithm for detection of an unknown change in the variance is also proposed. Simulation results illustrate characteristic properties of the algorithms including detection performance in terms of detection delay and false alarm rate. They also show that the theoretical analysis connected to the problem of detecting change in the mean can be extended to the problem of detecting change in the variance.