Open Access

ARTICLE

A Measurement Study of the Ethereum Underlying P2P Network

by Mohammad Z. Masoud¹, Yousef Jaradat¹, Ahmad Manasrah², Mohammad Alia³, Khaled Suwais^4,*, Sally Almanasra⁴

1 Electrical Engineering Department, Al-Zaytoonah University of Jordan, Amman, 11733, Jordan
2 Mechanical Engineering Department, Al-Zaytoonah University of Jordan, Amman, 11733, Jordan
3 Computer Science Department, Al-Zaytoonah University of Jordan, Amman, 11733, Jordan
4 Faculty of Computer Studies, Arab Open University, Riyadh, 11681, Saudi Arabia

* Corresponding Author: Khaled Suwais. Email:

Computers, Materials & Continua 2024, 78(1), 515-532. https://doi.org/10.32604/cmc.2023.044504

Received 01 August 2023; Accepted 15 November 2023; Issue published 30 January 2024

Abstract

This work carried out a measurement study of the Ethereum Peer-to-Peer (P2P) network to gain a better understanding of the underlying nodes. Ethereum was applied because it pioneered distributed applications, smart contracts, and Web3. Moreover, its application layer language “Solidity” is widely used in smart contracts across different public and private blockchains. To this end, we wrote a new Ethereum client based on Geth to collect Ethereum node information. Moreover, various web scrapers have been written to collect nodes’ historical data from the Internet Archive and the Wayback Machine project. The collected data has been compared with two other services that harvest the number of Ethereum nodes. Our method has collected more than 30% more than the other services. The data trained a neural network model regarding time series to predict the number of online nodes in the future. Our findings show that there are less than 20% of the same nodes daily, indicating that most nodes in the network change frequently. It poses a question of the stability of the network. Furthermore, historical data shows that the top ten countries with Ethereum clients have not changed since 2016. The popular operating system of the underlying nodes has shifted from Windows to Linux over time, increasing node security. The results have also shown that the number of Middle East and North Africa (MENA) Ethereum nodes is neglected compared with nodes recorded from other regions. It opens the door for developing new mechanisms to encourage users from these regions to contribute to this technology. Finally, the model has been trained and demonstrated an accuracy of 92% in predicting the future number of nodes in the Ethereum network.

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Keywords

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