Distributed Quickest Detection of Significant Events in Networks

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 statisti...

Full description

Saved in:
Bibliographic Details
Published inProceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 8454 - 8458
Main Authors Zou, Shaofeng, Veeravalli, Venugopal V., Li, Jian, Towsley, Don, Swami, Ananthram
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2019
Subjects
ADMM
average consensus
Computational modeling
Convex functions
Delays
distributed algorithm
Distributed algorithms
Event detection
network event detection
Optimization
quickest change detection
Sensors
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:2379-190X
DOI:10.1109/ICASSP.2019.8682316