Generalized analysis of a distributed energy efficient algorithm for change detection
Proceedings of the 12th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
Bayesian quickest change process detection
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
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
Cooperative quickest spectrum sensing in cognitive radios with unknown parameters
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Consensus-based Page's test in sensor networks
Signal Processing
A distributed self-adaptive nonparametric change-detection test for sensor/actuator networks
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
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The quickest change detection problem is studied in decentralized decision systems, where a set of sensors receive independent observations and send summary messages to the fusion center, which makes a final decision. In the system where the sensors do not have access to their past observations, the previously conjectured asymptotic optimality of a procedure with a monotone likelihood ratio quantizer (MLRQ) is proved. In the case of additive Gaussian sensor noise, if the signal-to-noise ratios (SNR) at some sensors are sufficiently high, this procedure can perform as well as the optimal centralized procedure that has access to all the sensor observations. Even if all SNRs are low, its detection delay will be at most pi/2-1ap 57% larger than that of the optimal centralized procedure. Next, in the system where the sensors have full access to their past observations, the first asymptotically optimal procedure in the literature is developed. Surprisingly, the procedure has the same asymptotic performance as the optimal centralized procedure, although it may perform poorly in some practical situations because of slow asymptotic convergence. Finally, it is shown that neither past message information nor the feedback from the fusion center improves the asymptotic performance in the simplest model