Efficient Data Gathering in Wireless Sensor Networks Based on Matrix Completion and Compressive Sensing

Jiping Xiong, Jian Zhao, Lei Chen


Gathering data in an energy efficient manner in
wireless sensor networks is an important design challenge. In wireless sensor networks, the readings of sensors always exhibit intra-temporal and inter-spatial correlations. Therefore, in this paper, we use low rank matrix completion theory to explore the inter-spatial correlation and use compressive sensing theory to take advantage of intratemporal correlation. Our method, dubbed MCCS, can significantly reduce the amount of data that each sensor must send through network and to the sink, thus prolong the lifetime of the whole networks. Experiments using real datasets demonstrate the feasibility and efficacy of our MCCS method


Data Gathering; Wireless senor Networks; Matrix completion; Compressive Sensing

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International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
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