Towards an online energy allocation policy for distributed estimation with sensor collaboration using energy harvesting sensors

Sijia Liu, Vinod Sharma, Pramod K. Varshney

Research output: Chapter in Book/Entry/PoemConference contribution

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages500-504
Number of pages5
ISBN (Electronic)9781509045457
DOIs
StatePublished - Apr 19 2017
Event2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States
Duration: Dec 7 2016Dec 9 2016

Publication series

Name2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings

Other

Other2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
Country/TerritoryUnited States
CityWashington
Period12/7/1612/9/16

Keywords

  • Distributed estimation
  • Energy harvesting
  • Semidefinite programming
  • Sensor collaboration

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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