TSD-PBFT:基于信誉和标准差聚类的PBFT共识优化算法  

TSD-PBFT:PBFT consensus optimization algorithm based ontrust and standard deviation clustering

作　　者：张丽 邓小鸿 石亦燃 刘勇 刘力汇 Zhang Li;Deng Xiaohong;Shi Yiran;Liu Yong;Liu Lihui(School of Information Engineering,Jiangxi University of Science&Technology,Ganzhou Jiangxi 341000,China;School of Information Engineering,Gannan University of Science&Technology,Ganzhou Jiangxi 341000,China;Key Laboratory of Cloud Computing&Big Data,Ganzhou Jiangxi 341000,China)

机构地区：[1]江西理工大学信息工程学院,江西赣州341000 [2]赣南科技学院信息工程学院,江西赣州341000 [3]赣州市云计算与大数据重点实验室,江西赣州341000

出　　处：《计算机应用研究》2025年第3期677-686,共10页Application Research of Computers

基　　金：国家自然科学基金资助项目(61762046,62166019);江西省自然科学基金资助项目(20224BAB202019)。

摘　　要：针对实用拜占庭容错共识算法中存在缺少对恶意节点的惩罚机制、通信开销大、主节点选取安全性不足等问题,提出了一种基于信誉和标准差聚类的PBFT共识优化算法TSD-PBFT,旨在提高共识效率和安全性。首先,建立节点动态和静态结合的信誉评估模型,通过实时监测节点投票数和参与度来动态评估节点行为,并剔除恶意节点来提高整体共识效率和可靠性,同时通过周期性地重置高信誉值节点的评分,防止单一节点或小团体长期主导共识过程;其次,提出基于信誉和标准差的聚类算法,引入标准差逐步选取密度高且信誉良好的节点作为聚类中心,避免局部最优解;同时采用改进的K-medoids聚类算法将节点分组并形成两层,实现分层共识来降低共识过程的通信开销;最后,优化主节点选取方式,由聚类中心节点投票产生主节点,通过赋予信誉高且标准差低的节点更高的投票权重来降低恶意节点担任主节点的概率,提高主节点选取的安全性和公正性。实验仿真结果表明,在相同的网络设置和节点数量条件下,与PBFT相比,TSD-PBFT算法平均吞吐量提高了72.1%,平均时延降低了50.2%。与现在类似PBFT改进算法相比,TSD-PBFT也具有明显的性能优势,能更好的适用于大规模联盟链场景。Aiming to address the lack of punishment mechanisms for malicious nodes,high communication overhead,and insufficient security in master node selection within practical Byzantine fault-tolerant consensus algorithms,this paper proposed a PBFT consensus optimization algorithm based on trust and standard deviation clustering,called TSD-PBFT,which aimed to improve the consensus efficiency and security.Firstly,the algorithm established a dynamic and static trust assessment model to evaluate node behavior in real-time by monitoring node votes and participation.This process eliminated malicious nodes to enhance overall consensus efficiency and reliability.Additionally,the scores of nodes with high trust values were periodically reset to prevent individual nodes or small groups from dominating the consensus process for extended periods.Secondly,the algorithm introduced a clustering approach based on trust and standard deviation.It selected nodes with high density and strong trust as clustering centers by incorporating standard deviation,which avoided local optimal solutions.The improved K-medoids clustering algorithm grouped the nodes into two layers,facilitating layered consensus and reducing communication overhead during the consensus process.Finally,the algorithm optimized the master node selection method.Nodes at the clustering centers voted to choose the master node,giving higher voting weights to those with high trust and low standard deviation.This approach reduced the likelihood of malicious nodes becoming the master node and enhanced the security and fairness of the selection process.Experimental simulation results demonstrate that,under the same network settings and number of nodes,TSD-PBFT algorithm improves average throughput by 72.1%and reduces average delay by 50.2%compared to PBFT.TSD-PBFT also shows significant performance advantages over similar PBFT improvements,making it more suitable for large-scale consortium blockchain scenarios.

关 键 词：区块链 共识机制 实用拜占庭容错 信誉机制 标准差聚类 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]