TY - GEN
T1 - Distributed Quickest Detection of Significant Events in Networks
AU - Zou, Shaofeng
AU - Veeravalli, Venugopal V.
AU - Li, Jian
AU - Towsley, Don
AU - Swami, Ananthram
N1 - Funding Information:
Research reported in this paper was sponsored in part by the Army Research Laboratory under Cooperative Agreement W911NF-17-2-0196 (IoBT CRA). The work of S. Zou and V. V. Veeravalli was also supported in part by the National Science Foundation (NSF) under grant CCF 16-18658, and by the Air Force Office of Scientific Research (AFOSR) under grant FA9550-16-1-0077.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - 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.
AB - 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.
KW - ADMM
KW - average consensus
KW - distributed algorithm
KW - network event detection
KW - quickest change detection
UR - http://www.scopus.com/inward/record.url?scp=85069005459&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069005459&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2019.8682316
DO - 10.1109/ICASSP.2019.8682316
M3 - Conference contribution
AN - SCOPUS:85069005459
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 8454
EP - 8458
BT - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Y2 - 12 May 2019 through 17 May 2019
ER -