A distributed wavelet compression algorithm for wireless sensor networks using lifting
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.