Analysis of noise impact on distributed average consensus

Boyuan Li; Henry Leung; Chatura Seneviratne

doi:10.1117/12.2303926

27 April 2018 Analysis of noise impact on distributed average consensus

Boyuan Li, Henry Leung, Chatura Seneviratne

Proceedings Volume 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII; 106460L (2018) https://doi.org/10.1117/12.2303926
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

This paper considers the problem of distributed average consensus in a noisy sensor network in which noise will cause error bits and detriment the accuracy of the results. We use a bit-flipping model to model the noise effect and show that it will lead to biased results. We propose here an unbiased average consensus algorithm for noisy networks with dynamic topologies. We analyze the convergence speed and the mean square error and show that the noise can be suppressed by our method. The proposed algorithm is found effective in a network simulation with and without perfect bit error rate information.

Conference Presentation

Citation Download Citation

Boyuan Li, Henry Leung, and Chatura Seneviratne "Analysis of noise impact on distributed average consensus", Proc. SPIE 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 106460L (27 April 2018); https://doi.org/10.1117/12.2303926

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