Distributed average consensus with quantization refinement

Abstract

We consider the problem of distributed average consensus in a sensor network where sensors exchange quantized information with their neighbors. We propose a novel quantization scheme that exploits the increasing correlation between the values exchanged by the sensors throughout the iterations of the consensus algorithm. A low complexity, uniform quantizer is implemented in each sensor, and refined quantization is achieved by progressively reducing the quantization intervals during the convergence of the consensus algorithm.We propose a recurrence relation for computing the quantization parameters that depend on the network topology and the communication rate. We further show that the recurre..