An L1-minimization based algorithm to measure the redundancy of state estimators in large sensor systems

Vishnu Vijayaraghavan, Kiavash Kianfar, Yu DIng, Hamid Parsaei

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

Abstract

Linear models have been successfully used to establish the connections between sensor measurements and system states in sensor networks. Finding the degree of redundancy for structured linear systems is proven to be NP-hard. Previously bound-and-decompose, 0-1 mixed integer programming and hybrid algorithms embedding 0-1 mixed integer feasibility checking within a bound-and-decompose framework have all been proposed and compared in the literature. In this paper, we exploit the computational efficiency of linear programs to present a novel heuristic algorithm which solves a series of l1-norm minimization problems in a specific framework to find extremely good solutions to this problem in remarkably small runtime.

Original languageEnglish
Title of host publication2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017
PublisherIEEE Computer Society
Pages424-428
Number of pages5
Volume2017-August
ISBN (Electronic)9781509067800
DOIs
Publication statusPublished - 12 Jan 2018
Event13th IEEE Conference on Automation Science and Engineering, CASE 2017 - Xi'an, China
Duration: 20 Aug 201723 Aug 2017

Other

Other13th IEEE Conference on Automation Science and Engineering, CASE 2017
CountryChina
CityXi'an
Period20/8/1723/8/17

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ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Vijayaraghavan, V., Kianfar, K., DIng, Y., & Parsaei, H. (2018). An L1-minimization based algorithm to measure the redundancy of state estimators in large sensor systems. In 2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017 (Vol. 2017-August, pp. 424-428). IEEE Computer Society. https://doi.org/10.1109/COASE.2017.8256141