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Distributed Binary Consensus in Networks with Disturbances

Distributed Binary Consensus in Networks with Disturbances Distributed Binary Consensus in Networks with Disturbances ALEXANDER GOGOLEV and NIKOLAJ MARCHENKO, University of Klagenfurt LUCIO MARCENARO, University of Genoa CHRISTIAN BETTSTETTER, University of Klagenfurt, Lakeside Labs GmbH This article evaluates convergence rates of binary majority consensus algorithms in networks with different types of disturbances and studies the potential capacity of randomization to foster convergence. Simulation results show that (a) additive noise, topology randomness, and stochastic message loss may improve the convergence rate; (b) presence of faulty nodes degrades the convergence rate; and (c) explicit randomization of consensus algorithms can be exploited to improve the convergence rate. Watts-Strogatz and Waxman graphs are used as underlying network topologies. A consensus algorithm is proposed that exchanges state information with dynamically randomly selected neighbors and, through this randomization, achieves almost sure convergence in some scenarios. Categories and Subject Descriptors: C.2.4 [Computer-Communication Networks]: Distributed Systems; C.4 [Performance of Systems]: Fault Tolerance General Terms: Algorithms, Performance, Fault Tolerance Additional Key Words and Phrases: Consensus algorithms, randomization, faulty nodes, Byzantine fault tolerance, delayed system, self-organization, density classification, majority sorting ACM Reference Format: Alexander Gogolev, Nikolaj Marchenko, Lucio Marcenaro, and Christian Bettstetter. 2015. Distributed binary consensus in networks with disturbances. ACM Trans. Auton. Adapt. Syst. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Autonomous and Adaptive Systems (TAAS) Association for Computing Machinery

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2015 by ACM Inc.
ISSN
1556-4665
DOI
10.1145/2746347
Publisher site
See Article on Publisher Site

Abstract

Distributed Binary Consensus in Networks with Disturbances ALEXANDER GOGOLEV and NIKOLAJ MARCHENKO, University of Klagenfurt LUCIO MARCENARO, University of Genoa CHRISTIAN BETTSTETTER, University of Klagenfurt, Lakeside Labs GmbH This article evaluates convergence rates of binary majority consensus algorithms in networks with different types of disturbances and studies the potential capacity of randomization to foster convergence. Simulation results show that (a) additive noise, topology randomness, and stochastic message loss may improve the convergence rate; (b) presence of faulty nodes degrades the convergence rate; and (c) explicit randomization of consensus algorithms can be exploited to improve the convergence rate. Watts-Strogatz and Waxman graphs are used as underlying network topologies. A consensus algorithm is proposed that exchanges state information with dynamically randomly selected neighbors and, through this randomization, achieves almost sure convergence in some scenarios. Categories and Subject Descriptors: C.2.4 [Computer-Communication Networks]: Distributed Systems; C.4 [Performance of Systems]: Fault Tolerance General Terms: Algorithms, Performance, Fault Tolerance Additional Key Words and Phrases: Consensus algorithms, randomization, faulty nodes, Byzantine fault tolerance, delayed system, self-organization, density classification, majority sorting ACM Reference Format: Alexander Gogolev, Nikolaj Marchenko, Lucio Marcenaro, and Christian Bettstetter. 2015. Distributed binary consensus in networks with disturbances. ACM Trans. Auton. Adapt. Syst.

Journal

ACM Transactions on Autonomous and Adaptive Systems (TAAS)Association for Computing Machinery

Published: Sep 8, 2015

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