Distributed change detection based on a randomized consensus algorithm

In this paper a novel consensus based distributed recursive algorithm is proposed for real time change detection using sensor networks. The algorithm is based on local statistics generated by geometric moving average control charts, and does not require any fusion center, so that the state of any node can be tested w.r.t. a given common threshold. Convergence of the algorithm to the optimal centralized solution defined by a weighted sum of the results of local signal processing is analyzed in the case of time varying random consensus gains, encompassing asymmetric "gossip" schemes and lossy networks, assuming correlated data and different local values of the parameter changes. Simulation results illustrate characteristic properties of the algorithms.