A low-complexity universal scheme for rate-constrained distributed regression using a wireless sensor network

We propose a scheme for rate-constrained distributed non-parametric regression using a wireless sensor network. The scheme is universal across a wide range of sensor noise models, including unbounded and nonadditive noise; it has low complexity, requiring simple operations such as uniform scalar quantization with dither and message passing between neighboring nodes in the network; and attains minimax optimality for regression functions in common smoothness classes. We present theoretical results on the trade-off between the compression rate and the MSE and demonstrate empirical performance of the scheme using simulations.