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基于动态信任的区块链激励共识机制研究
引用本文:周健,张杰,闫石,屈冉.基于动态信任的区块链激励共识机制研究[J].计算机应用研究,2021,38(11):3231-3235,3248.
作者姓名:周健  张杰  闫石  屈冉
作者单位:安徽财经大学管理科学与工程学院,安徽蚌埠233000;北京邮电大学计算机学院,北京100876;安徽财经大学管理科学与工程学院,安徽蚌埠233000
基金项目:国家自然科学基金资助项目(61402001);安徽省高等学校自然基金资助项目(KJ2020A0013,KJ2019A0657,KJ2018A0441);安徽财经大学研究生科研创新基金资助项目(ACYC2020349)
摘    要:针对实用拜占庭容错(PBFT)共识机制中主节点可能出错导致大量通信资源浪费的问题,提出一种基于信任动态激励的共识机制优化方案ITPBFT(incentive trust-PBFT).利用信任评价模型对系统内节点的共识行为进行评价,添加动态激励机制,引入节点等级机制,选取信任度高的节点作为主节点.通过对优化方案进行仿真实验和分析,验证了该机制可以有效提高共识机制的容错能力、降低共识的时间、提高系统的吞吐量并提高网络波动时的稳定性和可恢复性.该优化机制可有效提高共识的效率,避免通信资源的浪费.

关 键 词:区块链  共识机制  PBFT  信任评价  动态激励
收稿时间:2021/3/19 0:00:00
修稿时间:2021/10/13 0:00:00

Study on consensus mechanism of blockchain motivation based on dynamic trust
Zhou Jian,Zhang Jie,Yan Shi and Qu Ran.Study on consensus mechanism of blockchain motivation based on dynamic trust[J].Application Research of Computers,2021,38(11):3231-3235,3248.
Authors:Zhou Jian  Zhang Jie  Yan Shi and Qu Ran
Affiliation:School of Management Science and Engineering,Anhui University of Finance and Economics,Bengbu,,,
Abstract:Aiming at the problem that the master node in the practical Byzantine fault tolerance(PBFT) consensus mechanism may go wrong and cause a large amount of waste of communication resources, this paper proposed a consensus mechanism optimization scheme based on trust dynamic incentives, ITPBFT(incentive trust-PBFT). It evaluated the consensus behavior of nodes, added a dynamic incentive mechanism, introduced a node level mechanism, and selected a node with a high degree of trust as the master node. The simulation experiment and analysis of the optimization scheme verify that the mechanism can effectively improve the fault tolerance of the consensus mechanism, reduce the consensus time, increase the throughput of the system, and improve the stability and recoverability during network fluctuations. This consensus mechanism can effectively improve the efficiency of consensus and avoid waste of communication resources.
Keywords:blockchain  consensus mechanism  PBFT  trust evaluation  dynamic incentives
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