Research on PBFT consensus algorithm for grouping based on feature trust

被引:0
|
作者
Yong Wang
Meiling Zhong
Tong Cheng
机构
[1] Guilin University of Electronic Technology,School of Computer Science and Information Security
来源
关键词
D O I
暂无
中图分类号
学科分类号
摘要
The consensus mechanism is the core of the blockchain system, which plays an important role in the performance and security of the blockchain system . The Practical Byzantine Fault Tolerance (PBFT) algorithm is a widely used consensus algorithm, but the PBFT algorithm also suffers from high consensus latency, low throughput and performance. In this paper, we propose a grouped PBFT consensus algorithm (GPBFT) based on feature trust. First, the algorithm evaluates the trust degree of nodes in the transaction process through the EigenTrust trust model, and uses the trust degree of nodes as the basis for electing master nodes and proxy nodes. Then, the algorithm divides the nodes in the blockchain system into multiple groups, and the consensus within each independent group does not affect the other groups, which greatly reduces the communication overhead of the consensus process when the number of nodes in the system is large. Finally, we demonstrate through theoretical and experimental analysis that the GPBFT algorithm has a significant improvement in security and performance.
引用
收藏
相关论文
共 50 条
  • [1] Research on PBFT consensus algorithm for grouping based on feature trust
    Wang, Yong
    Zhong, Meiling
    Cheng, Tong
    SCIENTIFIC REPORTS, 2022, 12 (01)
  • [2] An improved PBFT consensus algorithm based on grouping and credit grading
    Liu, Shannan
    Zhang, Ronghua
    Liu, Changzheng
    Xu, Chenxi
    Wang, Jiaojiao
    SCIENTIFIC REPORTS, 2023, 13 (01):
  • [3] An improved PBFT consensus algorithm based on grouping and credit grading
    Shannan Liu
    Ronghua Zhang
    Changzheng Liu
    Chenxi Xu
    Jiaojiao Wang
    Scientific Reports, 13 (1)
  • [4] Improvement Research of PBFT Consensus Algorithm Based on Credit
    Wang, Yong
    Song, Zhe
    Cheng, Tong
    BLOCKCHAIN AND TRUSTWORTHY SYSTEMS, BLOCKSYS 2019, 2020, 1156 : 47 - 59
  • [5] CG-PBFT: an efficient PBFT algorithm based on credit grouping
    Juan Liu
    Xiaohong Deng
    Wangchun Li
    Kangting Li
    Journal of Cloud Computing, 13
  • [6] CG-PBFT: an efficient PBFT algorithm based on credit grouping
    Liu, Juan
    Deng, Xiaohong
    Li, Wangchun
    Li, Kangting
    JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS, 2024, 13 (01):
  • [7] An Extensible Consensus Algorithm Based on PBFT
    Li, Yixin
    Wang, Zhen
    Fan, Jia
    Zheng, Yue
    Luo, Yili
    Deng, Chunhua
    Ding, Jianwei
    2019 INTERNATIONAL CONFERENCE ON CYBER-ENABLED DISTRIBUTED COMPUTING AND KNOWLEDGE DISCOVERY (CYBERC), 2019, : 17 - 23
  • [8] Slicing PBFT Consensus Algorithm Based on VRF
    Chen, Pengyu
    Chen, Yuling
    Tan, Chaoyue
    Yang, Yuxiang
    Li, Bo
    Huang, Jiachen
    2024 IEEE INTERNATIONAL CONFERENCE ON BLOCKCHAIN, BLOCKCHAIN 2024, 2024, : 569 - 574
  • [9] Improvement of the PBFT Algorithm Based on Grouping and Reputation Value Voting
    Liu, Shannan
    Zhang, Ronghua
    Liu, Changzheng
    Xu, Chenxi
    Zhou, Jie
    Wang, Jiaojiao
    INTERNATIONAL JOURNAL OF DIGITAL CRIME AND FORENSICS, 2022, 14 (03)
  • [10] An improved PBFT consensus algorithm based on reputation and gaming
    Li, Zhe
    Wang, Jinsong
    Li, Yi
    JOURNAL OF SUPERCOMPUTING, 2025, 81 (01):