Compressing Spatial and Temporal Correlated Data in Wireless Sensor Networks Based on Ring Topology

In this paper, we propose an algorithm for wavelet based spatio-temporal data compression in wireless sensor networks. By employing a ring topology, the algorithm is capable of supporting a broad scope of wavelets that can simultaneously explore the spatial and temporal correlations among the sensory data. Furthermore, the ring based topology is in particular effective in eliminating the “border effect” generally encountered by wavelet based schemes. We propose a “Hybrid” decomposition based wavelet transform instead of wavelet transform based on the common dyadic decomposition, since temporal compression is local and far cheaper than spatial compression in sensor networks. We show that the optimal level of wavelet transform is different due to diverse sensor network circumstances. Theoretically and experimentally, we conclude the proposed algorithm can effectively explore the spatial and temporal correlation in the sensory data and provide significant reduction in energy consumption and delay compared to other schemes.