On the Performance of Spatio-Temporal Compression Schemes in Cluster-based WSNs

Energy constraint is the major challenge to be considered in WSNs. Many research works have been interested to lossy compression as an appealing solution to prolong the network lifespan, proposing several compression approaches in various fields such as signal processing (transform-based algorithms, linear approximations and compressive sensing) and information theory (distributed source coding). However, these techniques are seldom evaluated one against another in a practical WSN using the same routing protocol and similar signal characteristics. In this paper, we assess the performance of temporal, spatial, and spatiotemporal compression schemes in a cluster-based WSN in terms of network lifespan, computational cost and data accuracy. The Obtained results have revealed that spatiotemporal compression techniques are the best choice to extend the network lifetime providing efficient data accuracy.