Sparsity-aware sensor collaboration for linear coherent estimation

Sijia Liu, Swarnendu Kar, Makan Fardad, Pramod K. Varshney

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


In the context of distributed estimation, we consider the problem of sensor collaboration, which refers to the act of sharing measurements with neighboring sensors prior to transmission to a fusion center. While incorporating the cost of sensor collaboration, we aim to find optimal sparse collaboration schemes subject to a certain information or energy constraint. Two types of sensor collaboration problems are studied: minimum energy with an information constraint; and maximum information with an energy constraint. To solve the resulting sensor collaboration problems, we present tractable optimization formulations and propose efficient methods that render near-optimal solutions in numerical experiments. We also explore the situation in which there is a cost associated with the involvement of each sensor in the estimation scheme. In such situations, the participating sensors must be chosen judiciously. We introduce a unified framework to jointly design the optimal sensor selection and collaboration schemes. For a given estimation performance, we empirically show that there exists a trade-off between sensor selection and sensor collaboration.

Original languageEnglish (US)
Article number7060716
Pages (from-to)2582-2596
Number of pages15
JournalIEEE Transactions on Signal Processing
Issue number10
StatePublished - May 15 2015


  • Alternating direction method of multipliers
  • convex relaxation
  • distributed estimation
  • reweighted \ell<inf>1</inf>
  • sensor collaboration
  • sparsity
  • wireless sensor networks

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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