重庆理工大学学报(自然科学) ›› 2023, Vol. 37 ›› Issue (7): 227-234.

• 信息·计算机 • 上一篇    下一篇

一种蒙特卡洛方法的区块链邻居节点优选策略

陈 卓,王国安,周 川   

  1. (重庆理工大学 计算机科学与工程学院,重庆 400054)
  • 出版日期:2023-08-15 发布日期:2023-08-15
  • 作者简介:陈卓,男,博士,副教授,主要从事区块链关键技术及应用、分布式机器学习研究,Email:chenzhuo@cqut.edu.cn; 通信作者 王国安,男,硕士研究生,主要从事区块链关键技术研究,Email:469152908@qq.com。

A blockchain neighbor node optimization strategy based on Monte Carlo method

  • Online:2023-08-15 Published:2023-08-15

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。

关键词: 蒙特卡洛方法, 淘汰概率, 邻居节点, 传播时延

Abstract: 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%.

中图分类号: 

  • TP301