TY - GEN
T1 - Distributed center and coverage region estimation in wireless sensor networks using diffusion adaptation
AU - Zhang, Sai
AU - Tepedelenlioglu, Cihan
AU - Spanias, Andreas
N1 - Funding Information:
The authors from Arizona State University are funded in part by the NSF award ECSS - 1307982, FRP project and the SenSIP Center, School of ECEE, Arizona State University.
Publisher Copyright:
© 2017 IEEE.
PY - 2018/4/10
Y1 - 2018/4/10
N2 - A fully distributed algorithm for estimating the center and coverage region of a wireless sensor network (WSN) is proposed. The proposed algorithm is useful in many applications, such as finding the required power for a certain level of connectivity in WSNs and localizing a service center in a network. The network coverage region is defined to be the smallest sphere that covers all the sensor nodes. The center and radius of the smallest covering sphere are estimated. The center estimation is formulated as a convex optimization problem using soft-max approximation. Then, diffusion adaptation is used for distributed optimization to estimate the center. After all the sensors obtain the center estimates, max consensus is used to calculate the radius distributively. The performance analysis of the proposed algorithm is provided, as a function of a design parameter controls the trade-off between the center estimation error and the convergence speed of the algorithm. Simulation results are provided.
AB - A fully distributed algorithm for estimating the center and coverage region of a wireless sensor network (WSN) is proposed. The proposed algorithm is useful in many applications, such as finding the required power for a certain level of connectivity in WSNs and localizing a service center in a network. The network coverage region is defined to be the smallest sphere that covers all the sensor nodes. The center and radius of the smallest covering sphere are estimated. The center estimation is formulated as a convex optimization problem using soft-max approximation. Then, diffusion adaptation is used for distributed optimization to estimate the center. After all the sensors obtain the center estimates, max consensus is used to calculate the radius distributively. The performance analysis of the proposed algorithm is provided, as a function of a design parameter controls the trade-off between the center estimation error and the convergence speed of the algorithm. Simulation results are provided.
KW - Diffusion Adaptation
KW - Max Consensus
KW - Network Center
KW - Network Radius
KW - Soft-max
KW - Wireless Sensor Networks
UR - http://www.scopus.com/inward/record.url?scp=85050966769&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050966769&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.2017.8335575
DO - 10.1109/ACSSC.2017.8335575
M3 - Conference contribution
AN - SCOPUS:85050966769
T3 - Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
SP - 1353
EP - 1357
BT - Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
A2 - Matthews, Michael B.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Y2 - 29 October 2017 through 1 November 2017
ER -