A hierarchical adaptive spatio-temporal data compression scheme for wireless sensor networks

How to reduce the number of transmissions or prolong the lifetime of wireless sensor networks significantly has become a great challenge. Based on the spatio-temporal correlations of sensory data, in this paper, we propose a hierarchical adaptive spatio-temporal data compression (HASDC) scheme to address this issue. The proposed compression scheme explores the temporal correlation of original sensory data by employing the discrete cosine transform and adaptive threshold compression algorithm (ATCA). And then, the cluster head node explores the spatial correlation among the compressed temporal readings by utilizing discrete wavelet transform (DWT) and ATCA. The HASDC scheme obtains better recovery quality and compression ratio by combining data sorting, ATCA and spatio-temporal compression concept. At the same time, according to the correlation of sensory data and the adaptive threshold value, the HASDC scheme can adjust the compression ratio adaptively, thus it's applicable to different physical scenarios. Finally, the simulation results confirm that the transformed coefficients are more concentrated than the ones without introducing DWT, and the proposed scheme outperforms other spatio-temporal schemes in terms of compression and recovery performances.