Distributed estimation in sensor networks with quality feedback: A general framework

Nicolo Michelusi, Urbashi Mitra

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

6 Scopus citations

Abstract

This paper presents a general framework for distributed estimation of a dynamical process in a Wireless Sensor Network (WSN) in which the Sensor Nodes (SNs) communicate their measurements to the Fusion Center (FC) via a single hop wireless channel. The SNs adapt their sensing/transmission strategy based on a minimal feedback message provided by the FC, which informs them on the estimation quality achieved. Intuitively, when the estimation quality is poor, the SNs react by sending accurate measurements, at higher cost. When the estimation quality is good, the SNs remain idle to preserve energy. The sensing/transmission strategy of each SN, the channel access, and the feedback from the FC are jointly optimized, with the objective of minimizing the mean squared error at the FC, given a cost constraint for each SN. It is shown that a performance gain can be achieved by exploiting the estimation quality feedback, over policies that do not exploit such information, while achieving scalability to large WSNs. Moreover, a low complexity myopic policy is provided, which is shown to achieve near-optimal performance.

Original languageEnglish (US)
Title of host publication2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
Pages1057-1060
Number of pages4
DOIs
StatePublished - 2013
Externally publishedYes
Event2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Austin, TX, United States
Duration: Dec 3 2013Dec 5 2013

Publication series

Name2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings

Other

Other2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013
CountryUnited States
CityAustin, TX
Period12/3/1312/5/13

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

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