Sustainable broadcasting in Blockchain Networks with Reinforcement Learning

TL;DR

This paper proposes a reinforcement learning-based method to improve block broadcasting in blockchain networks, aiming to reduce energy consumption and carbon footprint while handling network dynamics effectively.

Contribution

It introduces a novel RL-driven approach to optimize block propagation, with a combined simulator and RL environment for further research.

Findings

Improved block broadcasting efficiency over default schemes

Effective handling of network dynamics with RL approach

Potential for reducing blockchain energy consumption

Abstract

Recent estimates put the carbon footprint of Bitcoin and Ethereum at an average of 64 and 26 million tonnes of CO2 per year, respectively. To address this growing problem, several possible approaches have been proposed in the literature: creating alternative blockchain consensus mechanisms, applying redundancy reduction techniques, utilizing renewable energy sources, and employing energy-efficient devices, etc. In this paper, we follow the second avenue and propose an efficient approach based on reinforcement learning that improves the block broadcasting scheme in blockchain networks. The analysis and experimental results confirmed that the proposed improvement of the block propagation scheme could cleverly handle network dynamics and achieve better results than the default approach. Additionally, our technical integration of the simulator and developed RL environment can be used as a complete solution for further study of new schemes and protocols that use RL or other ML techniques.