We address the problem of compression for wireless sensor networks, where each of the sensors has limited power, and acquires data that should be sent to a central node. The final goal is to have a reconstructed version of the sampled field at the central node, with the sensors spending as little energy as possible. We propose a distributed compression algorithm for multihop, distributed sensor networks based on the lifting factorization of the wavelet transform that exploits the natural data flow in the network to aggregate data by computing partial wavelet coefficients that are refined as the data flows towards the central node. A key result of our work is that by performing partial computations we greatly reduce unnecessary transmission, significantly reducing the overall energy consumption.
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