Byzantine-Fault-Tolerant Consensus via Reinforcement Learning for Permissioned Blockchain-Empowered V2X Network

【Author】 Kim, Seungmo; Ibrahim, Ahmed S.

【Source】IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

【影响因子】5.009

【Abstract】Blockchain has been forming the central piece of various types of vehicle-to-everything (V2X) network for trusted data exchange. Recently, permissioned blockchains garner particular attention thanks to their improved scalability and diverse needs from different organizations. One representative example of permissioned blockchain is Hyperledger Fabric ("Fabric"). Due to its unique execute-order procedure, there is a critical need for a client to select an optimal number of peers. The interesting problem that this paper targets to address is the tradeoff in the number of peers: a too large number will degrade scalability while a too small number will make the network vulnerable to faulty nodes. This optimization issue gets especially challenging in V2X networks due to mobility of nodes: a transaction must be executed, and the associated block must be committed before the vehicle leaves a network. To this end, this paper proposes a mechanism for selecting an optimal set of peers based on reinforcement learning (RL) to keep a Fabric-empowered V2X network impervious to dynamicity due to mobility. We model the RL as a contextual multi-armed bandit (MAB) problem. The results demonstrate the outperformance of the proposed scheme. [Kim, Seungmo] Georgia Southern Univ, Dept Elect & Comp Engn, Statesboro, GA 30460 USA; [Ibrahim, Ahmed S.] Florida Int Univ, Dept Elect & Comp Engn, Miami, FL 33174 USA University System of Georgia; Georgia Southern University; State University System of Florida; Florida International University Kim, S (通讯作者)，Georgia Southern Univ, Dept Elect & Comp Engn, Statesboro, GA 30460 USA. seungmokim@georgiasouthern.edu; aibrahim@fiu.edu Georgia Department of Transportation [RP 20-03]; National Science Foundation [CNS-1816112] Georgia Department of Transportation; National Science Foundation(National Science Foundation (NSF)) The work of Seungmo Kim was supported by the Georgia Department of Transportation under Grant RP 20-03. The work of Ahmed S. Ibrahim was supported by National Science Foundation under Award no. CNS-1816112. 50 0 0 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2379-8858 2379-8904 IEEE T INTELL VEHICL IEEE T. Intell. Veh. JAN 2023 8 1 172 183 10.1109/TIV.2022.3168575 http://dx.doi.org/10.1109/TIV.2022.3168575 12 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Transportation Science & Technology Science Citation Index Expanded (SCI-EXPANDED) Computer Science; Engineering; Transportation 8R9QG 2023-05-06 WOS:000928220400018

【Keywords】Fabrics; Peer-to-peer computing; Blockchains; Vehicle-to-everything; Organizations; Scalability; Reinforcement learning; Connected vehicles; blockchain; hyperledger fabric; BFT; RL; MAB

【发表时间】2023 JAN

【收录时间】2023-05-15

【文献类型】理论模型

【主题类别】

区块链技术-核心技术-扩展方案