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 -