一种蒙特卡洛方法的区块链邻居节点优选策略  

A blockchain neighbor node optimization strategy based on Monte Carlo method

作　　者：陈卓[1] 王国安 周川 CHEN Zhuo;WANG Guo’an;ZHOU Chuan(College of Computer Science and Engineering,Chongqing University of Technology,Chongqing 400054,China)

机构地区：[1]重庆理工大学计算机科学与工程学院,重庆400054

出　　处：《重庆理工大学学报（自然科学）》2023年第7期227-234,共8页Journal of Chongqing University of Technology：Natural Science

基　　金：国家自然科学基金项目(61471089,61401076);重庆市技术创新与应用发展基金项目(cstc2018jszx-cyztzx0088)。

摘　　要：针对目前区块链网络中区块传播耗时长、网络拓扑传输性能差的问题,设计了一种基于蒙特卡洛方法改进的邻居节点优选策略。首先,通过每轮区块到达节点的时间求得节点与邻居节点间的评分;然后,根据当前邻居节点的淘汰率从候选节点中随机添加新节点放入当前节点的邻居集,算出全部可能会被淘汰的组合,再利用蒙特卡洛方法和Softmax函数得到每个组合可能被淘汰的概率;最后,根据当前邻居节点的淘汰概率,从网络中随机选择节点替换当前邻居节点。仿真结果表明:与随机选择邻居节点的策略相比,邻居节点优选策略能提升区块在区块链网络中的传播效率,使区块的平均传播时间缩短30%左右。Aiming at the problems of long block propagation time and poor blockchain network topology transmission performance in the current blockchain network,this paper designs an improved neighbor node optimization strategy based on Monte Carlo method.Firstly,the strategy calculates the score between a node and its neighbor node by the time of each round of blocks arriving at the node.Then,the strategy randomly adds new nodes from the candidate nodes into the neighbor set of the current nodes according to the elimination rate of the current neighbor nodes,and the strategy calculates all possible elimination combinations.The strategy then uses Monte Carlo method and Softmax function to obtain the probability that each combination may be eliminated.Finally,the strategy randomly selects nodes from the network to replace the current neighbor nodes according to the probability of elimination of the current neighbor nodes.The simulation experiments show that,compared with the strategy of randomly selecting neighbor nodes,the neighbor node optimization strategy can improve the propagation efficiency of blocks in the blockchain network,which reduces the average propagation time of the blocks by about 30%.

关 键 词：蒙特卡洛方法 淘汰概率 邻居节点 传播时延 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]