  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 letter, we use low rank matrix completion theory to explore
the inter-spatial correlation and use compressive sensing theory to take
advantage of intra-temporal 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.
