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

TL;DR

This paper investigates distributed average consensus in sensor networks with quantized data and random link failures, proposing methods to achieve asymptotic consensus with controllable error and probability bounds.

Contribution

It introduces a novel analysis of dithered consensus with quantized data under random link failures, including boundedness proofs and quantizer parameter design strategies.

Findings

Consensus is asymptotically achieved with probability one.

Mean squared error can be minimized by tuning link weights.

Quantizer parameters can be designed to balance accuracy and saturation probability.

Abstract

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. To study dithered consensus with random links when the range of the quantizer is bounded, we establish uniform boundedness of the sample paths of the unbounded quantizer. This requires characterization of the statistical properties of the supremum taken over the sample paths of the state of the quantizer. This is accomplished by splitting the state vector of the quantizer in two components: one along the consensus subspace and the other along the subspace orthogonal to the consensus subspace. The proofs use maximal inequalities for submartingale and supermartingale sequences. From these, we derive probability bounds on the excursions of the two subsequences, from which probability bounds on the excursions of the quantizer state vector follow. The paper shows how to use these probability bounds to design the quantizer parameters and to explore tradeoffs among the number of quantizer levels, the size of the quantization steps, the desired probability of saturation, and the desired level of accuracy $\epsilon$ away from consensus. Finally, the paper illustrates the quantizer design with a numerical study.