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

Blockchain has been at the center of various trustpromoting applications for vehicle-to-everything (V2X) networks. Recently, permissioned blockchains gain practical popularity thanks to their improved scalability and diverse needs for different organizations. One representative example of permissioned blockchain is Hyperledger Fabric. Due to its unique executeorder 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 lead to a lower scalability and a too small number will leave a narrow margin in the number of peers sufficing the Byzantine fault tolerance (BFT). This channel selection issue gets especially challenging to deal with in V2X networks due to the mobility: a transaction must be executed and the associated block must be committed before the vehicle leaves a network. To this end, this paper proposes an optimal channel selection mechanism based on reinforcement learning (RL) to keep a Hyperledger Fabric-empowered V2X network impervious to dynamicity due to mobility. We model the RL as a contextual multi-armed bandit (MAB) problem. The results prove the outperformance of the proposed scheme.