A hybrid adaptive spatiotemporal compression scheme in cluster-based wireless sensor networks

Energy efficiency and data reliability are the crucial issues to be explored in wireless sensor networks (WSNs). A large amount of power consumption is depleted by each sensor node to transmit its sensed data which typically induces high temporal and spatial dependencies. Therefore, efficient compression approaches are designed to exploit the data correlation in both space and time so as to discard information redundancies with accurate recovery. In this paper, we propose a hybrid and adaptive spatiotemporal compression approach named Distributed Temporal Source Coding, designed to extend the lifespan of cluster based WSNs. The proposed scheme takes advantage of the performed Distributed Source Coding as an effective spatial compression algorithm and adaptively exploits the Lightweight Temporal Compression as a promising solution to reduce the temporally correlated information. The obtained results have revealed that the proposed algorithm outperforms other compression methods, promotes the network battery life, and ensures compelling data accuracy. The proposed scheme has succeeded to reduce the gap between the energy efficiency and data reliability in WSNs.