Sustainable broadcasting in Blockchain Network with Reinforcement Learning

2024-07-22

arXiv.org

AI Summary

To address the high carbon footprint of public blockchains (e.g., Bitcoin, Ethereum) induced by inefficient block broadcasting, this paper proposes a deep reinforcement learning–based dynamic adaptive broadcast optimization mechanism. We pioneer the integration of Deep Q-Networks (DQN) into peer-to-peer blockchain broadcast protocols, enabling real-time modeling of network topology, node load, and link conditions. A reward function is designed to jointly optimize energy efficiency and propagation latency, facilitating online, adaptive broadcast policy decisions in simulation environments. Experimental results demonstrate that our approach reduces average broadcast latency by 32% and bandwidth consumption by 27% compared to baseline protocols, while significantly lowering carbon emissions—without compromising compatibility with mainstream decentralized architectures. This work establishes a deployable, protocol-layer green optimization paradigm for sustainable blockchain scalability.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Economics, Online Markets and Human Computation: Economic aspects of blockchain and cryptocurrencies
• Security and Privacy: Blockchains and distributed ledgers
• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT

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.

Problem

• Reduce blockchain energy consumption via reinforcement learning
• Optimize block broadcasting to handle network dynamics
• Integrate simulator and RL for testing new protocols

Innovation

• Reinforcement learning optimizes block broadcasting
• Simulator and RL environment integrated technically
• Handles network dynamics better than default