SBFT:A BFT Consensus Mechanism Based on DQN Algorithm for Industrial Internet of Thing

查看全文 作　　者：Ningjie [1]Gao;Ru [2,3]Huo;Shuo [1,3]Wang;Jiang [1,3]Liu;Tao [1,3]Huang;Yunjie [1,2,3]Liu 机构地区：[1]State Key Laboratory of Networking and Switching Technology,Beijing University of Posts and Telecommunications,Beijing 100876,China;[2]Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China;[3]Future Network Research Center,Purple Mountain Laboratories,Nanjing 211111,China 出　　处：《China Communications》索引2023年第20卷第10期,共15页 摘　　要：With the development and widespread use of blockchain in recent years,many projects have introduced blockchain technology to solve the growing security issues of the Industrial Internet of Things(IIoT).However,due to the conflict between the operational performance and security of the blockchain system and the compatibility issues with a large number of IIoT devices running together,the mainstream blockchain system cannot be applied to IIoT scenarios.In order to solve these problems,this paper proposes SBFT(Speculative Byzantine Consensus Protocol),a flexible and scalable blockchain consensus mechanism for the Industrial Internet of Things.SBFT has a consensus process based on speculation,improving the throughput and consensus speed of blockchain systems and reducing communication overhead.In order to improve the compatibility and scalability of the blockchain system,we select some nodes to participate in the consensus,and these nodes have better performance in the network.Since multiple properties determine node performance,we abstract the node selection problem as a joint optimization problem and use Dueling Deep Q Learning(DQL)to solve it.Finally,we evaluate the performance of the scheme through simulation,and the simulation results prove the superiority of our scheme. 关 键 词：Industrial Internet of Things Byzantine fault tolerance speculative consensus mechanism Markov decision process deep reinforcement learning