Efficient sensor selection with application to time varying graphs

Buddhika L. Samarakoon, Manohar Murthi, Kamal Premaratne

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

Abstract

This paper addresses the problem of efficiently selecting sensors such that the mean squared estimation error is minimized under jointly Gaussian assumptions. First, we propose an O(n3) algorithm that yields the same set of sensors as a previously published near mean squared error (MSE) optimal method that runs in O(n4). Then we show that this approach can be extended to efficient sensor selection in a time varying graph. We consider a rank one modification to the graph Laplacian, which captures the cases where a new edge is added or deleted, or an edge weight is changed, for a fixed set of vertices. We show that we can efficiently update the new set of sensors in O(n2) time for the best case by saving computations that were done for the original graph. Experiments demonstrate advantages in computational time and MSE accuracy in the proposed methods compared to recently developed graph sampling methods.

Original languageEnglish (US)
Title of host publication2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
Volume2017-December
ISBN (Electronic)9781538612514
DOIs
StatePublished - Mar 9 2018
Event7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 - Curacao
Duration: Dec 10 2017Dec 13 2017

Other

Other7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
CityCuracao
Period12/10/1712/13/17

ASJC Scopus subject areas

  • Signal Processing
  • Control and Optimization
  • Instrumentation

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