推荐信誉模型与聚类分析协同优化的拜占庭算法

Byzantine Algorithm for Collaborative Optimization of Recommendation Reputation Model and Cluster Analysis

针对传统PBFT(practical Byzantine fault tolerance)共识算法主节点随机选择、通信复杂度和共识时延高等问题,提出了一种基于推荐信誉模型和聚类分析的改进拜占庭容错算法。根据推荐信誉模型,利用节点之间的交易行为计算出节点的全局信任值。在此基础上,利用全局信任值将节点划分为共识节点,非共识节点以及主组节点,并使全局信任值最高的节点成为主节点,可以大大降低恶意节点成为主节点的概率,从而提高了系统的效率。在每一轮共识结束后,对共识节点根据其特征进行聚类划分,进一步更新节点的全局信任值。通过仿真实验分析得出,改进后的TK-PBFT算法的共识时延降低了25%,通信开销成本减少超过50%,且具有更高的吞吐量。

A modified Byzantine fault-tolerant algorithm based on recommendation reputation model and clustering analysis is proposed to address the issues of random selection of main nodes,communication complexity,and high consensus delay in traditional PBFT(practical Byzantine fault tolerance)consensus algorithm.Firstly,based on the recommendation reputation model,the global trust value of nodes is calculated using the transaction behavior between nodes.On this basis,using global trust values to divide nodes into consensus nodes,non consensus nodes,and main group nodes,and making the node with the highest global trust value the main node can greatly reduce the probability of malicious nodes becoming the main node,thereby improving the efficiency of the system.Finally,after each round of consensus,the consensus nodes are clustered and divided based on their characteristics,further updating the global trust value of the nodes.Through simulation experiment analysis,it is found that the improved TK-PBFT algorithm reduces consensus latency by 25%,reduces communication overhead costs by more than 50%,and has higher throughput.