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Inner For-Loop for Speeding Up Blockchain Mining
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0002-5582-2031
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0003-4029-6574
2020 (English)In: Open Computer Science, ISSN 2299-1093, Vol. 10, no 1, p. 42-47Article in journal (Refereed) Published
Abstract [en]

In this paper, the authors propose to increase the efficiency of blockchain mining by using a population-based approach. Blockchain relies on solving difficult mathematical problems as proof-of-work within a network before blocks are added to the chain. Brute force approach, advocated by some as the fastest algorithm for solving partial hash collisions and implemented in Bitcoin blockchain, implies exhaustive, sequential search. It involves incrementing the nonce (number) of the header by one, then taking a double SHA-256 hash at each instance and comparing it with a target value to ascertain if lower than that target. It excessively consumes both time and power. In this paper, the authors, therefore, suggest using an inner for-loop for the population-based approach. Comparison shows that it’s a slightly faster approach than brute force, with an average speed advantage of about 1.67% or 3,420 iterations per second and 73% of the time performing better. Also, we observed that the more the total particles deployed, the better the performance until a pivotal point. Furthermore, a recommendation on taming the excessive use of power by networks, like Bitcoin’s, by using penalty by consensus is suggested.

Place, publisher, year, edition, pages
Poland: Walter de Gruyter, 2020. Vol. 10, no 1, p. 42-47
Keywords [en]
Blockchain, Network, Inner For-Loop, SHA-256, Brute force
National Category
Computer Sciences
Research subject
Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-76859DOI: 10.1515/comp-2020-0004Scopus ID: 2-s2.0-85081908099OAI: oai:DiVA.org:ltu-76859DiVA, id: diva2:1373899
Available from: 2019-11-28 Created: 2019-11-28 Last updated: 2020-03-27

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Adewumi, OluwatosinLiwicki, Marcus

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