On optimal sensor collaboration for distributed estimation with individual power constraints

Sijia Liu, Swarnendu Kar, Makan Fardad, Pramod Kumar Varshney

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In the context of distributed estimation, we study the problem of sensor collaboration with individual power constraints, where sensor collaboration refers to the act of sharing measurements with neighboring sensors prior to transmission to a fusion center. In order to find the optimal collaboration strategy consisting of collaboration topology and power allocation scheme, we propose a non-convex formulation in which the estimation distortion is minimized subject to individual power constraints. By exploiting the problem structure, locally optimal collaboration strategies are found via bilinear relaxations and a convex-concave procedure. Numerical examples are provided to show the effectiveness of our approach.

Original languageEnglish (US)
Title of host publicationConference Record - Asilomar Conference on Signals, Systems and Computers
PublisherIEEE Computer Society
Pages571-575
Number of pages5
Volume2016-February
ISBN (Print)9781467385763
DOIs
StatePublished - Feb 26 2016
Event49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States
Duration: Nov 8 2015Nov 11 2015

Other

Other49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
CountryUnited States
CityPacific Grove
Period11/8/1511/11/15

Keywords

  • bilinear relaxation
  • convex-concave procedure
  • Distributed estimation
  • networks
  • sensor collaboration
  • ℓ1 norm

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing

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  • Cite this

    Liu, S., Kar, S., Fardad, M., & Varshney, P. K. (2016). On optimal sensor collaboration for distributed estimation with individual power constraints. In Conference Record - Asilomar Conference on Signals, Systems and Computers (Vol. 2016-February, pp. 571-575). [7421194] IEEE Computer Society. https://doi.org/10.1109/ACSSC.2015.7421194