Compressive network coding for wireless sensor networks: Spatio-temporal coding and optimization design

Considering the temporal and spatial correlations of sensor readings in wireless sensor networks (WSNs), this paper develops a clustered spatio-temporal compression scheme by integrating network coding (NC), compressed sensing (CS) and spatio-temporal compression for correlated data. The proper selection of NC coefficients and measurement matrix is investigated for this scheme. This design ensures successful reconstruction of original data with a considerably high probability and enables successful deployment of NC and CS in a real field. Moreover, in contrast to other spatio-temporal schemes with the same computational complexity, the proposed scheme possesses lower reconstruction error by employing independent encoding in each sensor node (including the cluster head nodes) and joint decoding in the sink node. In order to further reduce the reconstruction error, we construct a new optimization model of reconstruction error for the clustered spatio-temporal compression scheme. A distributed algorithm is developed to iteratively determine the optimal solution. Finally, simulation results verify that the clustered spatio-temporal compression scheme outperforms other two categories of compression schemes significantly in terms of recovery error and compression gain and the distributed algorithm converges to the optimal solution with a fast and stable speed.