Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/8282
Title: Adaptive overclocking mining algorithm selection approach in the cryptocurrency mining pool
Authors: Dr. YUEN Man-Ching, Connie 
Lau, Ka-Ming 
Yung, Chi-Wai 
Ng, Ka-Fai 
Issue Date: 2022
Source: Yuen, Man-Ching, Lau, Ka-Ming, Yung, Chi-Wai & Ng, Ka-Fai (2022 Dec 16-18). Adaptive overclocking mining algorithm selection approach in the cryptocurrency mining pool. In ICBTA (Ed.). ICBTA '22: Proceedings of the 2022 5th International Conference on Blockchain Technology and Applications. ICBTA 2022: 2022 5th International Conference on Blockchain Technology and Applications, Xi'an China (pp. 50-56). Association for Computing Machinery.
Conference: ICBTA 2022: 2022 5th International Conference on Blockchain Technology and Applications 
Abstract: Cryptocurrency is one of the hit topics under the web 3.0 era. The first and most widely used cryptocurrency is bitcoin. For example, miners use bitcoin mining mechanism to solve cryptographic puzzles in a mining competition and receive Bitcoins as a reward. Setting the ideal clock rate on the mining device can produce the highest hash rate and thus the highest Bitcoin reward, which is one of the key factors affecting the results of the mining competition. In this paper, we concentrate on the NiceHash platform, the biggest crypto-mining market with a significant number of active miners. For miners, NiceHash offers software that can automatically choose the most lucrative algorithm, removing the need for them to keep an eye on the market and each of their various wallets. However, the current software typically chooses a mining algorithm for a miner to use without any further updates for a considerable amount of time, even though the profit made by using a mining algorithm is highly variable. Additionally, it chooses a mining algorithm without considering the clock rate of the miner's mining equipment. Furthermore, a lot of miners on the NiceHash pool encountered frequent disconnects. To solve the aforementioned issues, we propose a system that automatically selects the most profitable mining algorithms, adaptively sets the ideal clock rate of the mining device, and monitors the connection between miners and the mining pool. Compared to the original algorithm used in NiceHash, experimental results demonstrate that our automated system can efficiently maximize mining profits by increasing them by 21%. The profit can increase by up to 42% by having the mining device's clock rate automatically adjusted.
Type: Conference Paper
URI: http://hdl.handle.net/20.500.11861/8282
ISBN: 978-1-4503-9757-5
DOI: https://doi.org/10.1145/3581971.3581978
Appears in Collections:Applied Data Science - Publication

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