  In this paper, we consider the parameter estimation problem over sensor
networks in the presence of quantized data and directed communication links. We
propose a two-stage algorithm aiming at achieving the centralized sample mean
estimate in a distributed manner. Different from the existing algorithms, a
running average technique is utilized in the proposed algorithm to smear out
the randomness caused by the probabilistic quantization scheme. With the
running average technique, it is shown that the centralized sample mean
estimate can be achieved both in the mean square and almost sure senses, which
is not observed in the conventional consensus algorithms. In addition, the
rates of convergence are given to quantify the mean square and almost sure
performances. Finally, simulation results are presented to illustrate the
effectiveness of the proposed algorithm and highlight the improvements by using
running average technique.
