Energy aware iterative source localization for wireless sensor networks

Engin Maşazade, Ruixin Niu, Pramod K. Varshney, Mehmet Keskinoz

Research output: Contribution to journalArticlepeer-review

105 Scopus citations


In this paper, the source localization problem in wireless sensor networks is investigated where the location of the source is estimated based on the quantized measurements received from sensors in the field. An energy efficient iterative source localization scheme is proposed where the algorithm begins with a coarse location estimate obtained from measurement data from a set of anchor sensors. Based on the available data at each iteration, the posterior probability density function (pdf) of the source location is approximated using an importance sampling based Monte Carlo method and this information is utilized to activate a number of non-anchor sensors. Two sensor selection metrics namely the mutual information and the posterior CramrRao lower bound (PCRLB) are employed and their performance compared. Further, the approximate posterior pdf of the source location is used to compress the quantized data of each activated sensor using distributed data compression techniques. Simulation results show that with significantly less computation, the PCRLB based iterative sensor selection method achieves similar mean squared error (MSE) performance as compared to the state-of-the-art mutual information based sensor selection method. By selecting only the most informative sensors and compressing their data prior to transmission to the fusion center, the iterative source localization method reduces the communication requirements significantly and thereby results in energy savings.

Original languageEnglish (US)
Article number5475303
Pages (from-to)4824-4835
Number of pages12
JournalIEEE Transactions on Signal Processing
Issue number9
StatePublished - Sep 2010


  • Distributed source coding
  • Monte Carlo methods
  • posterior CramrRao lower bound
  • source localization
  • wireless sensor networks

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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