Joint Source-Network Coding for Large-Scale Sensor Networks
A modular system architecture based on separate compression and network coding is known to be theoretically suboptimal for relevant classes of sensor networks with correlated sources. Motivated by this observation, the authors present a feasible solution for joint source and network coding with distortion constraints. By choosing encoders that are simple scalar index assignments, they are able to move the complexity to the destination decoder. Given the network topology and the correlation structure of the data, their algorithms solve the problem of finding encoder and decoder instances that minimize the mean square error of every sample.