Performance of Certain Decentralized Distributed Change Detection Procedures

We compare several decentralized change-point detection procedures for multisensor distributed systems when the information available for decision-making is distributed across a set of sensors. Asymptotically optimal procedures for two scenarios are presented. In the first scenario, the sensors send quantized versions of their observations to a fusion center where change detection is performed based on all the sensor messages. If in particular, the quantizers are binary, then the proposed binary CUSUM detection test is optimal in the class of tests with binary quantized data. In the second scenario, the sensors perform local change detection using the CUSUM procedures and send their final decisions to the fusion center for combining. The decision in favor of the change occurrence is made whenever CUSUM statistics at all sensors exceed thresholds. The latter decentralized procedure has the same first order asymptotic (as the false alarm rate is low) minimax operating characteristics as the globally optimal centralized detection procedure that has access to all the sensor observations. However, the presented Monte Carlo experiments for the Poisson example show that despite the fact that the procedure with local decisions is globally asymptotically optimal for a low false alarm rate, it performs worse than the procedure with binary quantization unless the false alarm rate is extremely low. In addition, two voting-type local decision based detection procedures are proposed and evaluated. Applications to network security (rapid detection of computer intrusions) are discussed