Distributed Consensus Algorithms in Sensor Networks: Quantized Data and Random Link Failures

Understand

The paper studies the problem of distributed average consensus in sensor networks with quantized data and random link failures.

• To achieve consensus, dither (small noise) is added to the sensor states before quantization.
• When the quantizer range is unbounded (countable number of quantizer levels), stochastic approximation shows that consensus is asymptotically achieved with probability one and in mean square to a finite random variable.
• We show that the meansquared error (m.s.e.) can be made arbitrarily small by tuning the link weight sequence, at a cost of the convergence rate of the algorithm.