Energy aware iterative source localization for wireless sensor networks

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

105 Citations (Scopus)

Abstract

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
Article number5475303
Pages (from-to)4824-4835
Number of pages12
JournalIEEE Transactions on Signal Processing
Volume58
Issue number9
DOIs
Publication statusPublished - Sept 2010
Externally publishedYes

Funding

Manuscript received October 07, 2009; May 06, 2010; accepted May 07, 2010. Date of publication June 01, 2010; date of current version August 11, 2010. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Dominic K. C. Ho. This work is supported in part by the ARO Grant W911NF-09-1-0244. The work of M. Keskinoz is supported by TUBITAK under Grant 105E161. This work was presented in part at the IEEE Third International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Aruba, December 13–16, 2009.

FundersFunder number
TUBITAK105E161
Army Research OfficeW911NF-09-1-0244

    Keywords

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

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