The Evolution and Optimization Strategies of a PBFT Consensus Algorithm for Consortium Blockchains

被引:0
作者
Yuan, Fujiang [1 ,2 ]
Huang, Xia [1 ]
Zheng, Long [2 ]
Wang, Lusheng [1 ]
Wang, Yuxin [3 ]
Yan, Xinming [4 ]
Gu, Shaojie [5 ]
Peng, Yanhong [1 ,6 ]
机构
[1] Chongqing Univ Technol, Coll Mech Engn, Chongqing 400054, Peoples R China
[2] Taiyuan Normal Univ, Sch Comp Sci & Technol, Taiyuan 030619, Peoples R China
[3] Jiangsu Univ Sci & Technol, Sch Energy & Power, Zhenjiang 212100, Peoples R China
[4] Nagoya Univ, Grad Sch Engn, Dept Micronanomech Sci & Engn, Nagoya 4648601, Japan
[5] Kumamoto Univ, Magnesium Res Ctr, Kumamoto 8608555, Japan
[6] Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
关键词
distributed systems; consensus algorithms; consortium blockchain; PBFT; RAFT CONSENSUS; INTERNET; SCHEME; PROOF; TIME;
D O I
10.3390/info16040268
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the rapid development of blockchain technology, consensus algorithms have become a significant research focus. Practical Byzantine Fault Tolerance (PBFT), as a widely used consensus mechanism in consortium blockchains, has undergone numerous enhancements in recent years. However, existing review studies primarily emphasize broad comparisons of different consensus algorithms and lack an in-depth exploration of PBFT optimization strategies. The lack of such a review makes it challenging for researchers and practitioners to identify the most effective optimizations for specific application scenarios. In this paper, we review the improvement schemes of PBFT from three key directions: communication complexity optimization, dynamic node management, and incentive mechanism integration. Specifically, we explore hierarchical networking, adaptive node selection, multi-leader view switching, and a hybrid consensus model incorporating staking and penalty mechanisms. Finally, this paper presents a comparative analysis of these optimization strategies, evaluates their applicability across various scenarios, and offers insights into future research directions for consensus algorithm design.
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页数:40
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