TY - GEN

T1 - Distributed Spectral Radius Estimation in Wireless Sensor Networks

AU - Muniraju, Gowtham

AU - Tepedelenlioglu, Cihan

AU - Spanias, Andreas

N1 - Funding Information:
The authors from Arizona State University are funded in part by the NSF CPS award 1646542 and the SenSIP Center, School of ECEE, Arizona State University.
Publisher Copyright:
© 2019 IEEE.

PY - 2019/11

Y1 - 2019/11

N2 - A distributed algorithm to compute the spectral radius of the graph in the presence of additive channel noise is proposed. The spectral radius of the graph is the eigenvalue with the largest magnitude of the adjacency matrix, and is a useful characterization of the network graph. Conventionally, centralized methods are used to compute the spectral radius, which involves eigenvalue decomposition of the adjacency matrix of the underlying graph. We devise an algorithm to reach consensus on the spectral radius of the graph using only local neighbor communications, both in the presence and absence of additive channel noise. The algorithm uses a distributed max update to compute the growth rate in the node state values and then performs a specific update to converge on the logarithm of the spectral radius. The algorithm works for any connected graph structure. Simulation results supporting the theory are also presented.

AB - A distributed algorithm to compute the spectral radius of the graph in the presence of additive channel noise is proposed. The spectral radius of the graph is the eigenvalue with the largest magnitude of the adjacency matrix, and is a useful characterization of the network graph. Conventionally, centralized methods are used to compute the spectral radius, which involves eigenvalue decomposition of the adjacency matrix of the underlying graph. We devise an algorithm to reach consensus on the spectral radius of the graph using only local neighbor communications, both in the presence and absence of additive channel noise. The algorithm uses a distributed max update to compute the growth rate in the node state values and then performs a specific update to converge on the logarithm of the spectral radius. The algorithm works for any connected graph structure. Simulation results supporting the theory are also presented.

KW - Wireless sensor network

KW - consensus

KW - distributed networks

KW - spectral radius

UR - http://www.scopus.com/inward/record.url?scp=85083303723&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85083303723&partnerID=8YFLogxK

U2 - 10.1109/IEEECONF44664.2019.9049018

DO - 10.1109/IEEECONF44664.2019.9049018

M3 - Conference contribution

AN - SCOPUS:85083303723

T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers

SP - 1506

EP - 1510

BT - Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019

A2 - Matthews, Michael B.

PB - IEEE Computer Society

T2 - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019

Y2 - 3 November 2019 through 6 November 2019

ER -