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Analysis of noise impact on distributed average consensus
Author(s): Boyuan Li; Henry Leung; Chatura Seneviratne
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Paper 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.

Paper Details

Date Published: 27 April 2018
PDF: 13 pages
Proc. SPIE 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 106460L (27 April 2018); doi: 10.1117/12.2303926
Show Author Affiliations
Boyuan Li, Univ. of Calgary (Canada)
Henry Leung, Univ. of Calgary (Canada)
Chatura Seneviratne, Univ. of Calgary (Canada)


Published in SPIE Proceedings Vol. 10646:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII
Ivan Kadar, Editor(s)

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