Clustered Spatio-Temporal Compression Design for Wireless Sensor Networks

Siguang Chen, Chuanxin Zhao, Meng Wu, Zhixin Sun, Jian Jin

Abstract: Since the temporal and spatial correlations of sensor readings are existent in wireless sensor networks (WSNs), this paper develops a clustered spatio-temporal compression scheme by integrating network coding (NC) and compressed sensing (CS) for correlated data. The proper selections of NC coefficients and measurement matrix are designed for this scheme. This design guarantees the reconstruction of clustered compression data successfully with an overwhelming probability and unifies the operations of NC and CS into real field successfully. Moreover, in contrast to other spatio-temporal schemes with the same computational complexity, the proposed scheme possesses lower reconstruction error by employing the independent encoding in each sensor node (including the cluster head nodes) and joint decoding in sink node. At the same time it has lower computational complexity as compared with JSM-based spatio-temporal scheme by exploiting the temporal and spatial correlations of original sensing data step by step. Finally, the simulation results verify that the clustered spatio-temporal compression scheme outperforms the other two compression schemes significantly in terms of recovery error and compression gain.