Energy-aware sensor selection in field reconstruction

Sijia Liu, Aditya Vempaty, Makan Fardad, Engin Masazade, Pramod K. Varshney

Research output: Contribution to journalArticlepeer-review

37 Scopus citations


In this letter, a new sparsity-promoting penalty function is introduced for sensor selection problems in field reconstruction, which has the property of avoiding scenarios where the same sensors are successively selected. Using a reweighted ℓ1 relaxation of the ℓ0 norm, the sensor selection problem is reformulated as a convex quadratic program. In order to handle large-scale problems, we also present two fast algorithms: accelerated proximal gradient method and alternating direction method of multipliers. Numerical results are provided to demonstrate the effectiveness of our approaches.

Original languageEnglish (US)
Article number6867304
Pages (from-to)1476-1480
Number of pages5
JournalIEEE Signal Processing Letters
Issue number12
StatePublished - Dec 2014


  • Alternating direction method of multipliers
  • convex relaxation
  • field reconstruction
  • proximal gradient method
  • sensor selection
  • sparsity

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics


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