Distributed Average Consensus using Probabilistic Quantization

Authors:
Tuncer C. Aysal;Mark Coates;Michael Rabbat
Affiliations:
Department of Electrical and Computer Engineering, McGill University, Montreal, QC. tuncer.aysal@mcgill.ca;Department of Electrical and Computer Engineering, McGill University, Montreal, QC. mark.coates@mcgill.ca;Department of Electrical and Computer Engineering, McGill University, Montreal, QC. michael.rabbat@mcgill.ca
Venue:
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Year:
2007

Citing 0
Cited 13

Distributed consensus algorithms in sensor networks with imperfect communication: link failures and channel noise

IEEE Transactions on Signal Processing
Network lifetime maximization for estimation in multihop wireless sensor networks

IEEE Transactions on Signal Processing
Broadcast gossip algorithms for consensus

IEEE Transactions on Signal Processing
Load balancing over heterogeneous networks with gossip-based algorithms

ACC'09 Proceedings of the 2009 conference on American Control Conference
Real-valued average consensus over noisy quantized channels

ACC'09 Proceedings of the 2009 conference on American Control Conference
Distributed consensus algorithms in sensor networks: quantized data and random link failures

IEEE Transactions on Signal Processing
Reaching consensus in wireless networks with probabilistic broadcast

Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Distributed anonymous function computation in information fusion and multiagent systems

Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Distributed lossy averaging

IEEE Transactions on Information Theory
Stochastic consensus over noisy networks with Markovian and arbitrary switches

Automatica (Journal of IFAC)
Convergence of consensus models with stochastic disturbances

IEEE Transactions on Information Theory
Convergence-optimal quantizer design of distributed contraction-based iterative algorithms with quantized message passing

IEEE Transactions on Signal Processing
Brief paper: Quantized consensus in Hamiltonian graphs

Automatica (Journal of IFAC)

Quantified Score

Hi-index	0.14

Visualization

Abstract

In this paper, we develop algorithms for distributed computation of averages of the node data over networks with bandwidth/power constraints or large volumes of data. Distributed averaging algorithms fail to achieve consensus when deterministic uniform quantization is adopted. We propose a distributed algorithm in which the nodes utilize probabilistically quantized information to communicate with each other. The algorithm we develop is a dynamical system that generates sequences achieving a consensus, which is one of the quantization values. In addition, we show that the expected value of the consensus is equal to the average of the original sensor data. We report the results of simulations conducted to evaluate the behavior and the effectiveness of the proposed algorithm in various scenarios.