Inner For-Loop for Speeding Up Blockchain Mining

Document identifier: oai:DiVA.org:ltu-76859
Access full text here:10.1515/comp-2020-0004
Keyword: Natural Sciences, Computer and Information Sciences, Computer Sciences, Naturvetenskap, Data- och informationsvetenskap, Datavetenskap (datalogi), Blockchain, Network, Inner For-Loop, SHA-256, Brute force, Maskininlärning, Machine Learning
Publication year: 2020
Relevant Sustainable Development Goals (SDGs):
SDG 9 Industry, innovation and infrastructure
The SDG label(s) above have been assigned by OSDG.ai

Abstract:

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.

Authors

Oluwatosin Adewumi

Luleå tekniska universitet; EISLAB
Other publications >>

Marcus Liwicki

Luleå tekniska universitet; EISLAB
Other publications >>

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