随机和噪声环境下的有效二元一致

2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP) Pub Date : 2014-04-21 DOI:10.1109/ISSNIP.2014.6827594

A. Gogolev, L. Marcenaro

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引用次数: 1

摘要

在本文中，我们研究了随机拓扑和噪声网络中的随机二元多数共识。仿真结果表明，异步简单多数规则在有更新偏差的随机邻居选择和少量误差的随机化条件下可以达到100%的收敛率。接下来，我们证明了这种增益对加性噪声和拓扑随机化具有鲁棒性。

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Efficient binary consensus in randomized and noisy environments

In this article we investigate randomized binary majority consensus in networks with random topologies and noise. Using computer simulations, we show that asynchronous Simple Majority rule can reach ≃ 100% convergence rate being randomized by an update-biased random neighbor selection and a small fraction of errors. Next, we show that such gains are robust towards additive noise and topology randomization.

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来源期刊

2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)

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