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...
Autores principales: | , , , , |
---|---|
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 |