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

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 exam...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on intelligent vehicles 2023-01, Vol.8 (1), p.172-183
Main Authors: Kim, Seungmo, Ibrahim, Ahmed S.
Format: Article
Language:English
Subjects:
BFT
Blockchain
Blockchains
Connected vehicles
Cryptography
Data exchange
Fabrics
Fault tolerance
hyperledger fabric
MAB
Machine learning
Nodes
Optimization
Organizations
Peer-to-peer computing
Reinforcement learning
Scalability
Vehicle-to-everything
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2379-8858
2379-8904
DOI:10.1109/TIV.2022.3168575