Compressive sensing based data collection in wireless sensor networks

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    Abstract

    Compressive sensing originates in the field of signal processing and has recently become a topic of energy-efficient data gathering in wireless sensor networks. In this paper, we introduce a distributed compressive sensing approach, which utilizes spatial correlation among sensor nodes to group them into coalitions. The coalition formation method is represented by a block diagonal measurement matrix whose each diagonal entity corresponds to one of the coalitions. Then, a spatial-temporal correlation-based compressive sensing approach is used inside each coalition to schedule sensor nodes and encode their readings. Distributed data encoding over coalitions increases robustness and scalability of the approach. Simulation results verify that the proposed solution outperforms other compressive sensing approaches significantly in terms of data accuracy and energy efficiency.
    Original languageEnglish
    Pages442-447
    DOIs
    Publication statusPublished - 18 Nov 2017
    Event2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017 - Daegu, Korea, Republic of
    Duration: 16 Nov 201718 Nov 2017

    Conference

    Conference2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2017
    Abbreviated titleMFI
    CountryKorea, Republic of
    CityDaegu
    Period16/11/1718/11/17

    Keywords

    • Compressive sensing
    • wireless sensor networks

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