  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
recurrence relation can lead to a simple exponential model for the size of the
quantization step size over the iterations, whose parameters can be computed a
priori. Finally, simulation results demonstrate the effectiveness of the
progressive quantization scheme that leads to the consensus solution even at
low communication rate.
