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

Abstract

Blockchain has been at the center of various applications in vehicle-to-everything (V2X) networking. Recently, permissioned blockchain gain practical popularity thanks to its improved scalability and diverse needs for different organizations. One representative example of permissioned blockchain is Hyperledger Fabric. Due to its unique execute-order procedure, there is a critical need for a client to select an optimal number of peers. There is a 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 more 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 a 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.