TY - GEN
T1 - Distributed Edge Counting for 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:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Consensus-based distributed algorithms to estimate the number of active edges in wireless sensor networks are proposed. Conventionally, a communication link between two nodes is formed if they have sufficient transmission power to exchange information. In power constrained networks, it is crucial to know the total number of active communication links so that additional links can be created or removed depending on the power budget. We develop two distributed algorithms to estimate number of edges in the network: (a) an average consensus based algorithm which involves distributed node counting (b) a max consensus based algorithm by using the expected value of maximum of random state values. We find that average consensus based algorithm is appropriate for smaller networks, whereas, max consensus based algorithm is faster and best suited for larger networks. We derive the variance of the estimators which is proportional to square of total number of edges and inversely proportional to the number of random vectors used for estimation. The simulation results supporting the theory are also presented.
AB - Consensus-based distributed algorithms to estimate the number of active edges in wireless sensor networks are proposed. Conventionally, a communication link between two nodes is formed if they have sufficient transmission power to exchange information. In power constrained networks, it is crucial to know the total number of active communication links so that additional links can be created or removed depending on the power budget. We develop two distributed algorithms to estimate number of edges in the network: (a) an average consensus based algorithm which involves distributed node counting (b) a max consensus based algorithm by using the expected value of maximum of random state values. We find that average consensus based algorithm is appropriate for smaller networks, whereas, max consensus based algorithm is faster and best suited for larger networks. We derive the variance of the estimators which is proportional to square of total number of edges and inversely proportional to the number of random vectors used for estimation. The simulation results supporting the theory are also presented.
KW - Distributed networks
KW - distributed consensus
KW - edge counting
KW - power allocation
UR - http://www.scopus.com/inward/record.url?scp=85127062709&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85127062709&partnerID=8YFLogxK
U2 - 10.1109/IEEECONF53345.2021.9723199
DO - 10.1109/IEEECONF53345.2021.9723199
M3 - Conference contribution
AN - SCOPUS:85127062709
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 767
EP - 771
BT - 55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
Y2 - 31 October 2021 through 3 November 2021
ER -