TY - GEN
T1 - Towards an online energy allocation policy for distributed estimation with sensor collaboration using energy harvesting sensors
AU - Liu, Sijia
AU - Sharma, Vinod
AU - Varshney, Pramod K.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/4/19
Y1 - 2017/4/19
N2 - We study an energy allocation problem for distributed estimation with sensor collaboration, where collaboration refers to the act of sharing measurements with neighboring sensors prior to transmission to the fusion center, and the sensors are equipped with energy harvesters to replenish their power from the environment. Based on the statistics of the harvested energy and dynamics of energy flow at each sensor, we propose a provably efficient online energy allocation policy for distributed estimation with sensor collaboration. The proposed online policy relies on solving an offline non-convex optimization problem, in which the estimation distortion is minimized subject to energy and network topology constraints. We employ semidefinite programming to find the globally-optimal solution of the non-convex problem. We show that the proposed online policy is asymptotically consistent and provide mean square error of the optimal offline solution over an infinite time horizon.
AB - We study an energy allocation problem for distributed estimation with sensor collaboration, where collaboration refers to the act of sharing measurements with neighboring sensors prior to transmission to the fusion center, and the sensors are equipped with energy harvesters to replenish their power from the environment. Based on the statistics of the harvested energy and dynamics of energy flow at each sensor, we propose a provably efficient online energy allocation policy for distributed estimation with sensor collaboration. The proposed online policy relies on solving an offline non-convex optimization problem, in which the estimation distortion is minimized subject to energy and network topology constraints. We employ semidefinite programming to find the globally-optimal solution of the non-convex problem. We show that the proposed online policy is asymptotically consistent and provide mean square error of the optimal offline solution over an infinite time horizon.
KW - Distributed estimation
KW - Energy harvesting
KW - Semidefinite programming
KW - Sensor collaboration
UR - http://www.scopus.com/inward/record.url?scp=85019263671&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019263671&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2016.7905892
DO - 10.1109/GlobalSIP.2016.7905892
M3 - Conference contribution
AN - SCOPUS:85019263671
T3 - 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
SP - 500
EP - 504
BT - 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
Y2 - 7 December 2016 through 9 December 2016
ER -