Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Seungmo Kim, Ahmed S. Ibrahim

TL;DR

An optimal peers selection mechanism based on reinforcement learning (RL) to keep a Fabric-empowered V2X network impervious to dynamicity due to mobility and the results demonstrate the outperformance of the proposed scheme.

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.

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

TL;DR

An optimal peers selection mechanism based on reinforcement learning (RL) to keep a Fabric-empowered V2X network impervious to dynamicity due to mobility and the results demonstrate the outperformance of the proposed scheme.

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.

Paper Structure

This paper contains 31 sections, 1 theorem, 4 equations, 7 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. Hyperledger Fabric
  4. Reinforcement Learning for Performance Optimization
  5. Contribution of This Paper
  6. System Model
  7. Blockchain
  8. Geometry
  9. Proposed Mechanism
  10. Improvement to the Hyperledger Fabric Architecture
  11. RL for Channel Selection
  12. Spatiotemporal View
  13. Problem Formulation
  14. Context
  15. Reward
  16. ...and 16 more sections

Key Result

Lemma 1

(Tradeoff on the number of peers). Regarding the constraint in (eq_problem), for a client, a tradeoff is formed in selecting a channel through which a transaction is executed and committed. In particular, the latency and throughput depend on "the number of peers." If there are too many peers, a high

Figures (7)

  • Figure 1: Proposed endorser selection mechanism
  • Figure 2: The simulation software structure
  • Figure 3: Average latency (in seconds) versus {number of peers, probability of failure}
  • Figure 4: Rate of dissemination of a message among a group of peers
  • Figure 5: Convergence of the proposed RL algorithm (With 10 channels; For each subfigure: Upper: Selected channel at each $t$, Lower: Probability of each channel selection over $t$)
  • ...and 2 more figures

Theorems & Definitions (4)

  • Remark 1
  • Lemma 1
  • Definition 1
  • Definition 2