A Dynamic Programming Approach to Distortion-Energy Optimization for Distributed Wavelet Compression with Applications to Data Gathering Inwireless Sensor Networks

We address a scenario where energy-constrained sensors in a wireless sensor network can choose among different distributed coding schemes to encode their data. We propose a framework where the network is described as a graph, with sensors representing the nodes, and where communication and processing costs are associated to edge weights and the coding schemes associated to states of operation. After describing data transitions and edge costs, we show that a shortest-path algorithm can be used to find the optimum network configuration, i.e., the one that leads to the lowest overall energy consumption