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Blockchain has been forming the central piece of various types ofvehicle-to-everything (V2X) network for trusted data exchange. Recently,permissioned blockchains garner particular attention thanks to their improvedscalability and diverse needs from different organizations. One representativeexample of permissioned blockchain is Hyperledger Fabric ("Fabric"). Due to itsunique execute-order procedure, there is a critical need for a client to selectan optimal number of peers. The interesting problem that this paper targets toaddress is the tradeoff in the number of peers: a too large number will degradescalability while a too small number will make the network vulnerable to faultynodes. This optimization issue gets especially challenging in V2X networks dueto mobility of nodes: a transaction must be executed and the associated blockmust be committed before the vehicle leaves a network. To this end, this paperproposes an optimal peers selection mechanism based on reinforcement learning(RL) to keep a Fabric-empowered V2X network impervious to dynamicity due tomobility. We model the RL as a contextual multi-armed bandit (MAB) problem. Theresults demonstrate the outperformance of the proposed scheme.

Byzantine-Fault-Tolerant Consensus via Reinforcement Learning for Permissioned Blockchain Implemented in a V2X Network