Distributed Quickest Detection of Significant Events in Networks
Shaofeng Zou, Venugopal V. Veeravalli, Jian Li, Don Towsley, Ananthram Swami · 2019
The problem of quickest detection of significant events in networks is studied. A distributed setting is investigated, where there is no fusion center, and each node only communicates with its neighbors. After an event occurs in the network, a number of nodes are affected, which changes the statistics of their observations. The nodes may possibly perceive the event at different times. The goal is to design a distributed sequential detection rule that can detect when the event is "significant", i.e., the event has affected no less than η nodes, as quickly as possible, subject to false alarm constraints. A distributed algorithm is proposed, which is based on a novel combination of the alternating direction method of multipliers (ADMM) and average consensus approaches. Numerical results are provided to demonstrate the performance of the proposed algorithm.