{"title":"随机和噪声环境下的有效二元一致","authors":"A. Gogolev, L. Marcenaro","doi":"10.1109/ISSNIP.2014.6827594","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Efficient binary consensus in randomized and noisy environments\",\"authors\":\"A. Gogolev, L. Marcenaro\",\"doi\":\"10.1109/ISSNIP.2014.6827594\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":269784,\"journal\":{\"name\":\"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSNIP.2014.6827594\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSNIP.2014.6827594","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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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