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.
Quickest Detection of Anomalies of Varying Location and Size in Sensor Networks
IEEE Transactions on Aerospace and Electronic Systems ; 57 , 4 ; 2109-2120
2021-08-01
847531 byte
Article (Journal)
Electronic Resource
English
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