Distributed Quickest Detection of Significant Events in Networks
Shaofeng Zou, Venugopal V. Veeravalli, Jian Li, Don Towsley, Ananthram Swami
Abstract
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.
BibTeX
@inproceedings{icassp2019_distributedquick,
title = {Distributed Quickest Detection of Significant Events in Networks},
author = {Shaofeng Zou and Venugopal V. Veeravalli and Jian Li and Don Towsley and Ananthram Swami},
booktitle = {ICASSP 2019},
year = {2019}
}