Zhejiang Hexin Flush Network Services Ltd
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, the authors use low rank matrix completion theory to explore the inter-spatial correlation and use compressive sensing theory to take advantage of intra-temporal correlation. Their 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 their MCCS method.