Balanced sensor management across multiple time instances via l-1/l-infinity norm minimization

Cristian Rusu, John Thompson, Neil M. Robertson

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

Abstract

In this paper, we propose a solution to the sensor management problem over multiple time instances that balances the accuracy of the sensor network estimation with its utilization. We show how this problem reduces to a binary optimization problem for which we give a convex relaxation based solution that involves the minimization of a regularized L-infinity reweighted L-1 norm. We show experimentally the behavior of the proposed algorithm and compare it with previous methods from the literature.
Original languageUndefined/Unknown
Title of host publication2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Pages3311-3315
Number of pages5
DOIs
Publication statusPublished - 1 Mar 2017

Keywords

  • estimation theory
  • minimisation
  • wireless sensor networks
  • L-1/L-infinity norm minimization
  • balanced sensor management
  • binary optimization problem
  • convex relaxation
  • multiple time instances
  • regularized L-infinity reweighted L-1 norm minimization
  • sensor network estimation
  • Electronic mail
  • Estimation
  • Linear programming
  • Measurement uncertainty
  • Minimization
  • Optimization
  • Volume measurement
  • binary optimization
  • convex optimization
  • sensor management

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