Change Detection of a Subset of High-Dimensional Time Series Data in Sensor Networks

We develop a new algorithm for change detection in sensor networks. We consider a general case where a network of sensors monitors the environment and sends observations to a Fusion Centre (FC). At an unknown time instant, the distribution of the observations of a subset of the sensor changes simultaneously. The objective is twofold: 1) detect which sensors have changed; and 2) estimate the time of the change. To this end we develop the Maximum A-Posteriori (MAP) estimator which involves solving an optimization problem via a combinatorial search that is computationally heavy. We overcome this problem by re-formulating the deterministic optimization problem as a stochastic one, which then can be solved efficiently via the Cross Entropy (CE) method, with only a minor performance degradation, compared with the optimal brute-force solution. Simulation results demonstrate the efficiency of our algorithm in solving the joint estimation problem.