A Computationally Efficient Algorithm for Quickest Change Detection in Anonymous Heterogeneous Sensor Networks

Abstract

The problem of quickest change detection in anonymous heterogeneous sensor networks is studied. The sensors are clustered into <tex>$K$</tex> groups, and different groups follow different data generating distributions. At some unknown time, an event occurs in the network and changes the data generating distribution of the sensors. The goal is to detect the change as quickly as possible, subject to false alarm constraints. The anonymous setting is studied, where at each time step, the fusion center receives unordered samples without knowing which sensor each sample comes from, and thus does not know its exact distribution. In [1], an optimal algorithm was provided, which however is not computational efficient for large networks. In this paper, a computationally efficient test is proposed and a novel theoretical characterization of its false alarm rate is further developed.