Quickest Detection of Anomalies of Varying Location and Size in Sensor Networks

The problem of sequentially detecting the emergence of a moving anomaly in a sensor network is studied. In the setting considered, the data-generating distribution at each sensor can alternate between a nonanomalous distribution and an anomalous distribution. Initially, the observations of each sensor are generated according to its associated nonanomalous distribution. At some unknown but deterministic time instant, a moving anomaly emerges in the network. It is assumed that the number as well as the identity of the sensors affected by the anomaly may vary with time. While a sensor is affected, it generates observations according to its corresponding anomalous distribution. The goal of this work is to design detection procedures to detect the emergence of such a moving anomaly as quickly as possible, subject to constraints on the frequency of false alarms. The problem is studied in a quickest change detection framework where it is assumed that the spatial evolution of the anomaly over time is unknown but deterministic. We modify the worst-path detection delay metric introduced in prior work on moving anomaly detection to consider the case of a moving anomaly of varying size. We then establish that a weighted dynamic cumulative sum type test is first-order asymptotically optimal under a delay-false alarm formulation for the proposed worst-path delay as the mean time to false alarm goes to infinity. We conclude by presenting numerical simulations to validate our theoretical analysis.