A distributed compressive sensing technique for data gathering in Wireless Sensor Networks

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    Abstract

    Compressive sensing is a new technique utilized for energy efficient data gathering in wireless sensor networks. It is characterized by its simple encoding and complex decoding. The strength of compressive sensing is its ability to reconstruct sparse or compressible signals from small number of measurements without requiring any a priori knowledge about the signal structure. Considering the fact that wireless sensor nodes are often deployed densely, the correlation among them can be utilized for further compression. By utilizing this spatial correlation, we propose a joint sparsity-based compressive sensing technique in this paper. Our approach employs Bayesian inference to build probabilistic model of the signals and thereafter applies belief propagation algorithm as a decoding method to recover the common sparse signal. The simulation results show significant gain in terms of signal reconstruction accuracy and energy consumption of our approach compared with existing approaches.
    Original languageUndefined
    Title of host publicationThe 4th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2013
    Place of PublicationAmsterdam
    PublisherElsevier
    Pages207-216
    Number of pages10
    DOIs
    Publication statusPublished - Oct 2013
    EventThe 4th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2013: The 4th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2013 - Niagara Falls, Ontario, Canada, Amsterdam
    Duration: 21 Oct 201324 Oct 2013

    Publication series

    NameProcedia Computer Science
    PublisherElsevier
    Volume21
    ISSN (Print)1877-0509
    ISSN (Electronic)1877-0509

    Workshop

    WorkshopThe 4th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2013
    CityAmsterdam
    Period21/10/1324/10/13
    Other21-24 Oct 2013

    Keywords

    • EWI-23891
    • IR-88333
    • METIS-300118

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