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The problem of detecting changes with multiple sensors has receivedsignificant attention in the literature. In many practical applications such ascritical infrastructure monitoring and modeling of disease spread, a usefulchange propagation model is one where change eventually happens at all sensors,but where not all sensors witness change at the same time instant. While priorwork considered the case of known change propagation dynamics, this paperstudies a more general setting of unknown change propagation pattern(trajectory). A Bayesian formulation of the problem in both centralized anddecentralized settings is studied with the goal of detecting the first timeinstant at which any sensor witnesses a change. Using the dynamic programming(DP) framework, the optimal solution structure is derived and in the rarechange regime, several more practical change detection algorithms are proposed.Under certain conditions, the first-order asymptotic optimality of a proposedalgorithm called multichart test is shown as the false alarm probabilityvanishes. To further reduce the computational complexity, change detectionalgorithms are proposed based on online estimation of the unknown changepropagation pattern. Numerical studies illustrate that the proposed detectiontechniques offer near-optimal performance. Further, in the decentralizedsetting, it is shown that if an event-triggered sampling scheme calledlevel-crossing sampling with hysteresis (LCSH) is used for sampling andtransmission of local statistics, the detection performance can besignificantly improved using the same amount of communication resourcescompared to the conventional uniform-in-time sampling (US) scheme.

Multi-Sensor Sequential Change Detection with Unknown Change Propagation Pattern