Authors: Umesh Bhatt, Sarvesh Pandey

Published on: March 26, 2024

Impact Score: 7.8

Arxiv code: Arxiv:2403.17885

Summary

• What is new: This paper examines the impact of Ethereum’s transition from PoW to PoS on miner dynamics and introduces effective methods to predict priority fees using machine learning models.
• Why this is important: The transition poses questions regarding its effects on miner participation and the challenge for users to set appropriate priority fees.
• What the research proposes: Conducts an empirical study on miner dynamics post-transition and employs machine learning models to predict priority fees.
• Results: Found increased miner participation and a larger pool of small miners, with reduced randomness in miner selection. Gradient Boosting Regressor was most effective in predicting priority fees.

Technical Details

Technological frameworks used: Regression analysis, machine learning

Models used: Gradient Boosting Regressor, K-Neighbours Regressor

Data used: Miner participation data, transaction fees

Potential Impact

Cryptocurrency exchanges, blockchain-based applications, and energy sectors could be affected.

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