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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 remote 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 wavelet algorithm, based on the lifting scheme, as a means to decorrelate data at the nodes by exchanging information between neighboring sensors. A key result of our work is that by using a locally adaptive distributed transform it is possible to optimize overall power consumption by operating at the right trade-off point between local processing and transmission costs.