Sensor placement for field estimation via Poisson disk sampling

Sijia Liu, Nianxia Cao, Pramod K. Varshney

Research output: Chapter in Book/Entry/PoemConference contribution

5 Scopus citations

Abstract

In this paper, we study the problem of sensor placement for field estimation, where the best subset of potential sensor locations is chosen to strike a balance between the number of deployed sensors and estimation accuracy. Potential sensor locations are generated by sampling a continuous field of interest. We investigate the impact of sampling strategies on sensor placement, and show that compared to other commonly-used sampling strategies, the Poisson disk sampling method can provide a more accurate (discretized) representation of the random field. Based on the sampled locations, we propose an efficient placement algorithm that scales gracefully with problem size using the alternating direction method of multipliers and the accelerated gradient descent method. Numerical results are provided to demonstrate the effectiveness of our approach for sensor placement.

Original languageEnglish (US)
Title of host publication2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages520-524
Number of pages5
ISBN (Electronic)9781509045457
DOIs
StatePublished - Apr 19 2017
Event2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States
Duration: Dec 7 2016Dec 9 2016

Publication series

Name2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings

Other

Other2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
Country/TerritoryUnited States
CityWashington
Period12/7/1612/9/16

Keywords

  • Alternating direction method of multipliers
  • Field estimation
  • Poisson disk sampling
  • Sensor placement
  • Sparsity

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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