Randomized Binary Consensus with Faulty Agents

Gogolev, Alexander

doi:10.3390/e16052820

Cited by 4 publications

(3 citation statements)

References 44 publications

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“…Table II gives an overview of some studies on BMC with randomization and faulty nodes in relation to the contributions of this article. Based on this article, the focus of Gogolev and Marcenaro [2014b] is a deeper investigation of the impact of faulty nodes on convergence rate in SM and GKL. …”

Section: Convergence Of Random Neighbors Majoritymentioning

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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Section: Convergence Of Random Neighbors Majoritymentioning

confidence: 99%

Distributed Binary Consensus in Networks with Disturbances

Gogolev

Marchenko

Bettstetter

2015

ACM Trans. Auton. Adapt. Syst.

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“…Gogolev and Marcenaro [60] study the problem of consensus [61], considering faulty nodes with random and persistent failures. Computer simulations are used to show that, counter-intuitively, different randomisations can actually increase the robustness of consensus, as the effect of faulty nodes can be reduced with noise, message loss or topology.…”

Section: Special Issuementioning

confidence: 99%

Entropy Methods in Guided Self-Organisation

Prokopenko

Gershenson

2014

Entropy

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Self-organisation occurs in natural phenomena when a spontaneous increase in order is produced by the interactions of elements of a complex system. Thermodynamically, this increase must be offset by production of entropy which, broadly speaking, can be understood as a decrease in order. Ideally, self-organisation can be used to guide the system towards a desired regime or state, while "exporting" the entropy to the system's exterior. Thus, Guided Self-Organisation (GSO) attempts to harness the order-inducing potential of self-organisation for specific purposes. Not surprisingly, general methods developed to study entropy can also be applied to guided self-organisation. This special issue covers a broad diversity of GSO approaches which can be classified in three categories: information theory, intelligent agents, and collective behavior. The proposals make another step towards a unifying theory of GSO which promises to impact numerous research fields.

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“…Connectivity between nodes also affect the consensus value for the gossip algorithm. Besides few randomized consensus algorithms [7], [8] also exist. This consensus algorithm does not provide guaranteed consensus for the additive noise in the network.…”

Section: Related Workmentioning

confidence: 99%

Efficient consensus algorithm for the accurate faulty node tracking with faster convergence rate in a distributed sensor network

Hossain

Khan

2016

J Wireless Com Network

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One of the challenging issues in a distributed computing system is to reach on a decision with the presence of so many faulty nodes. These faulty nodes may update the wrong information, provide misleading results and may be nodes with the depleted battery power. Consensus algorithms help to reach on a decision even with the faulty nodes. Every correct node decides some values by a consensus algorithm. If all correct nodes propose the same value, then all the nodes decide on that. Every correct node must agree on the same value. Faulty nodes do not reach on the decision that correct nodes agreed on. Binary consensus algorithm and average consensus algorithm are the most widely used consensus algorithm in a distributed system. We apply binary consensus and average consensus algorithm in a distributed sensor network with the presence of some faulty nodes. We evaluate these algorithms for better convergence rate and error rate.

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Randomized Binary Consensus with Faulty Agents

Cited by 4 publications

References 44 publications

Distributed Binary Consensus in Networks with Disturbances

Distributed Binary Consensus in Networks with Disturbances

Entropy Methods in Guided Self-Organisation

Efficient consensus algorithm for the accurate faulty node tracking with faster convergence rate in a distributed sensor network

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