Robustness of Self-Organizing Consensus Algorithms: Initial Results from a Simulation-Based Study

Gogolev, Alexander; Bettstetter, Christian

doi:10.1007/978-3-642-28583-7_11

Cited by 2 publications

(6 citation statements)

References 7 publications

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“…This work extends and complements previous investigations on binary majority consensus with stochastic elements [15,17,18,25,30,32] in terms of the robustness towards faulty node behavior.…”

Section: Discussionsupporting

confidence: 81%

“…The cluster-breaking impact of randomization also contributes to the convergence rate in the systems without faulty nodes. This effect was earlier observed in [15,18,25] and can be explained as follows. Binary majority consensus is designed to provide a common decision for all nodes in the system, so the stable clusters of nodes sharing a different state inhibit the convergence.…”

Section: Faulty Nodes With Random and Persistent Failuresupporting

confidence: 56%

“…Networked control algorithms, inspired by such systems, can be efficient and robust [24]. Binary majority consensus exhibits self-organizing features: it is performed by simple rules in a distributed manner and can show an increase in efficiency with stochastic intrusions [15,25]. Studies on self-organized consensus can provide practical insights on the engineering of networked control systems.…”

Section: Related Workmentioning

confidence: 99%

“…The impact of randomly failing faulty nodes is more strongly expressed than the impact of randomly failing faulty nodes with full failure, though both types indicate similar dependencies. This can hint to the fact that randomization within the consensus state space can be more efficient [15,17,25].…”

Section: Randomization By Noise and Message Loss In Random Networkmentioning

confidence: 99%

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Randomized Binary Consensus with Faulty Agents

Gogolev

2014

Entropy

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This paper investigates self-organizing binary majority consensus disturbed by faulty nodes with random and persistent failure. We study consensus in ordered and random networks with noise, message loss and delays. Using computer simulations, we show that: (1) explicit randomization by noise, message loss and topology can increase robustness towards faulty nodes; (2) commonly-used faulty nodes with random failure inhibit consensus less than faulty nodes with persistent failure; and (3) in some cases, such randomly failing faulty nodes can even promote agreement.

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Section: Discussionsupporting

confidence: 81%

Section: Faulty Nodes With Random and Persistent Failuresupporting

confidence: 56%

Section: Related Workmentioning

confidence: 99%

Section: Randomization By Noise and Message Loss In Random Networkmentioning

confidence: 99%

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Randomized Binary Consensus with Faulty Agents

Gogolev

2014

Entropy

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“…This can be partially explained by the randomization provided by communication errors and random topology, since some algorithms gain performance with randomization [Saks et al 1991;Aspnes 2002]. This motivated us to study the robustness of consensus in a larger range of system parameters (also see Gogolev and Bettstetter [2012]; Marcenaro [2014a, 2014b]). …”

Section: Introductionmentioning

confidence: 99%

Distributed Binary Consensus in Networks with Disturbances

Gogolev

Marchenko

Bettstetter

2015

ACM Trans. Auton. Adapt. Syst.

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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. ACM Reference Format:Alexander Gogolev, Nikolaj Marchenko, Lucio Marcenaro, and Christian Bettstetter. 2015. Distributed binary consensus in networks with disturbances.

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Robustness of Self-Organizing Consensus Algorithms: Initial Results from a Simulation-Based Study

Cited by 2 publications

References 7 publications

Randomized Binary Consensus with Faulty Agents

Randomized Binary Consensus with Faulty Agents

Distributed Binary Consensus in Networks with Disturbances

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