Cargando…

An improved PBFT consensus algorithm based on grouping and credit grading

To improve the blockchain consensus algorithm practical Byzantine fault tolerance (PBFT) with random master node selection, which has high communication overhead and a small supported network size, this paper proposes a Byzantine fault tolerant consensus algorithm based on credit (CBFT) enhanced wit...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Shannan, Zhang, Ronghua, Liu, Changzheng, Xu, Chenxi, Wang, Jiaojiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10415261/
https://www.ncbi.nlm.nih.gov/pubmed/37563158
http://dx.doi.org/10.1038/s41598-023-28856-x
_version_ 1785087495008944128
author Liu, Shannan
Zhang, Ronghua
Liu, Changzheng
Xu, Chenxi
Wang, Jiaojiao
author_facet Liu, Shannan
Zhang, Ronghua
Liu, Changzheng
Xu, Chenxi
Wang, Jiaojiao
author_sort Liu, Shannan
collection PubMed
description To improve the blockchain consensus algorithm practical Byzantine fault tolerance (PBFT) with random master node selection, which has high communication overhead and a small supported network size, this paper proposes a Byzantine fault tolerant consensus algorithm based on credit (CBFT) enhanced with a grouping and credit model. The CBFT algorithm divides the network nodes according to the speed of their response to the management nodes, resulting in different consensus sets, and achieves consensus within and outside the group separately to reduce communication overhead and increase system security. Second, the nodes are divided into different types according to the credit model, each with different responsibilities to reduce the probability that the master node is a malicious node. Experimental results show that the throughput of the CBFT algorithm is 3.1 times that of PBFT and 1.5 times that of GPBFT when the number of nodes is 52. Our scheme has latency that is 7.4% that of PBFT and 38.8% that of GPBFT; CBFT has communication overhead that is 6.4% that of PBFT and 87.3% that of GPBFT. The number of nodes is 300, and the Byzantine fault tolerance is improved by 59.3%. These improvements are clearer with the increase in the number of nodes.
format Online
Article
Text
id pubmed-10415261
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-104152612023-08-12 An improved PBFT consensus algorithm based on grouping and credit grading Liu, Shannan Zhang, Ronghua Liu, Changzheng Xu, Chenxi Wang, Jiaojiao Sci Rep Article To improve the blockchain consensus algorithm practical Byzantine fault tolerance (PBFT) with random master node selection, which has high communication overhead and a small supported network size, this paper proposes a Byzantine fault tolerant consensus algorithm based on credit (CBFT) enhanced with a grouping and credit model. The CBFT algorithm divides the network nodes according to the speed of their response to the management nodes, resulting in different consensus sets, and achieves consensus within and outside the group separately to reduce communication overhead and increase system security. Second, the nodes are divided into different types according to the credit model, each with different responsibilities to reduce the probability that the master node is a malicious node. Experimental results show that the throughput of the CBFT algorithm is 3.1 times that of PBFT and 1.5 times that of GPBFT when the number of nodes is 52. Our scheme has latency that is 7.4% that of PBFT and 38.8% that of GPBFT; CBFT has communication overhead that is 6.4% that of PBFT and 87.3% that of GPBFT. The number of nodes is 300, and the Byzantine fault tolerance is improved by 59.3%. These improvements are clearer with the increase in the number of nodes. Nature Publishing Group UK 2023-08-10 /pmc/articles/PMC10415261/ /pubmed/37563158 http://dx.doi.org/10.1038/s41598-023-28856-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Liu, Shannan
Zhang, Ronghua
Liu, Changzheng
Xu, Chenxi
Wang, Jiaojiao
An improved PBFT consensus algorithm based on grouping and credit grading
title An improved PBFT consensus algorithm based on grouping and credit grading
title_full An improved PBFT consensus algorithm based on grouping and credit grading
title_fullStr An improved PBFT consensus algorithm based on grouping and credit grading
title_full_unstemmed An improved PBFT consensus algorithm based on grouping and credit grading
title_short An improved PBFT consensus algorithm based on grouping and credit grading
title_sort improved pbft consensus algorithm based on grouping and credit grading
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10415261/
https://www.ncbi.nlm.nih.gov/pubmed/37563158
http://dx.doi.org/10.1038/s41598-023-28856-x
work_keys_str_mv AT liushannan animprovedpbftconsensusalgorithmbasedongroupingandcreditgrading
AT zhangronghua animprovedpbftconsensusalgorithmbasedongroupingandcreditgrading
AT liuchangzheng animprovedpbftconsensusalgorithmbasedongroupingandcreditgrading
AT xuchenxi animprovedpbftconsensusalgorithmbasedongroupingandcreditgrading
AT wangjiaojiao animprovedpbftconsensusalgorithmbasedongroupingandcreditgrading
AT liushannan improvedpbftconsensusalgorithmbasedongroupingandcreditgrading
AT zhangronghua improvedpbftconsensusalgorithmbasedongroupingandcreditgrading
AT liuchangzheng improvedpbftconsensusalgorithmbasedongroupingandcreditgrading
AT xuchenxi improvedpbftconsensusalgorithmbasedongroupingandcreditgrading
AT wangjiaojiao improvedpbftconsensusalgorithmbasedongroupingandcreditgrading