Quickest Dynamic Anomaly Detection in Anonymous Heterogeneous Sensor Networks

The problem of quickest dynamic anomaly detection in anonymous heterogeneous sensor networks is studied. The n heterogeneous sensors can be divided into K types with different data generating distributions. At some unknown time, an anomaly emerges in the network and changes the data generating distribution of the sensors. The goal is to detect the anomaly as quickly as possible, subject to false alarm constraints. The anonymous setting is studied, where the fusion center does not know which sensor that each sample comes from, and thus does not know its exact distribution. Firstly, the static setting is investigated where the sensor affected by the anomaly does not change with time. A generalized mixture CuSum algorithm is constructed and is further shown to be asymptotically optimal. The problem is then extended to a dynamic setting where the sensor affected by the anomaly changes with time. An asymptotically optimal weighted mixture CuSum algorithm is proposed. Numerical results are also provided to validate the theoretical results.