Bitplane coding for correlations exploitation in wireless sensor networks

In this paper, we propose a compression scheme called spatial set-partitioning in hierarchical trees which exploits the spatial and temporal correlations present in sensor data. This scheme allows progressive transmission and provides scalability in adapting to the underlying correlation structure of sensed data. It uses flexible Slepian-Wolf coding based on low density parity-check codes. Two different decoding schemes are proposed for different types of resource constrained sensor nodes. This scheme outperforms known codecs by a large margin of decibel in terms of the signal-to-noise ratio. This scheme has O(n) complexity for encoding and O(nlog(n)) complexity for decoding using message passing, where n is the codeword length. Experiments and simulation results with field data sets demonstrate the viability of our proposed scheme to wireless sensor networks.