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

TL;DR

This paper introduces a reinforcement learning-based method to optimize peer selection in permissioned blockchain V2X networks, enhancing scalability and fault tolerance amid node mobility.

Contribution

It presents a novel RL-based peer selection mechanism modeled as a contextual multi-armed bandit problem for blockchain in V2X networks.

Findings

The RL-based approach outperforms baseline methods.

Optimized peer selection improves network scalability.

Enhanced fault tolerance in dynamic V2X environments.

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 an optimal peers selection mechanism 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.