Broadcast gossip algorithms for consensus
IEEE Transactions on Signal Processing
Gossip consensus algorithms via quantized communication
Automatica (Journal of IFAC)
Distributed consensus with quantized data via sequence averaging
IEEE Transactions on Signal Processing
Distributed consensus algorithms in sensor networks: quantized data and random link failures
IEEE Transactions on Signal Processing
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
A simple and scalable algorithm for alignment in broadcast networks
IEEE Journal on Selected Areas in Communications - Special issue on simple wireless sensor networking solutions
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
Order-optimal consensus through randomized path averaging
IEEE Transactions on Information Theory
Information theoretic bounds for distributed computation over networks of point-to-point channels
IEEE Transactions on Information Theory
Binary consensus over fading channels
IEEE Transactions on Signal Processing
Brief paper: Distributed averaging on digital erasure networks
Automatica (Journal of IFAC)
Brief paper: Quantized consensus in Hamiltonian graphs
Automatica (Journal of IFAC)
Quantization, channel compensation, and optimal energy allocation for estimation in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Discontinuities and hysteresis in quantized average consensus
Automatica (Journal of IFAC)
Convergence time analysis of quantized gossip consensus on digraphs
Automatica (Journal of IFAC)
Generating dithering noise for maximum likelihood estimation from quantized data
Automatica (Journal of IFAC)
Consensus seeking over directed networks with limited information communication
Automatica (Journal of IFAC)
Hi-index | 0.21 |
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, i.e., dithered quantization, to communicate with each other. The algorithm we develop is a dynamical system that generates sequences achieving a consensus at one of the quantization values almost surely. In addition, we show that the expected value of the consensus is equal to the average of the original sensor data. We derive an upper bound on the mean-square-error performance of the probabilistically quantized distributed averaging (PQDA). Moreover, we show that the convergence of the PQDA is monotonic by studying the evolution of the minimum-length interval containing the node values. We reveal that the length of this interval is a monotonically nonincreasing function with limit zero. We also demonstrate that all the node values, in the worst case, converge to the final two quantization bins at the same rate as standard unquantized consensus. Finally, we report the results of simulations conducted to evaluate the behavior and the effectiveness of the proposed algorithm in various scenarios.