NONLINEAR PROOF-OF-WORK: IMPROVING THE ENERGY EFFICIENCY OF BITCOIN MINING

Main Article Content

Riaan Bezuidenhout
Wynand Nel
Andries Burger

Keywords

Bitcoin, bitcoin mining, consensus algorithm, nonlinear proof-of-work, proof-of-work

Abstract

Bitcoin is probably the most well-known blockchain system in existence. It employs the proof-of-work (PoW) consensus algorithm to add transactions to the blockchain. This process is better known as Bitcoin mining. PoW requires miners to compete in solving a cryptographic puzzle before being allowed to add a block of transactions to the blockchain. This mining process is energy-intensive and results in high energy wastage. The underlying cause of this energy inefficiency is the result of the current implementation of the PoW algorithm. PoW assigns the same cryptographic puzzle to all miners, creating a linear probability of success between the miner’s computational power as a proportion of the total computational power of the network. To address this energy inefficiency of the PoW mining process, the researchers investigated whether a nonlinear probability of success, between the miner’s computation power and its probability of success, will result in better energy usage. A nonlinear proof-of-work (nlPoW) algorithm was constructed by using a design science approach to derive the requirements for and structure of the algorithm. The Bitcoin mining process was tested through statistical simulation, comparing the performance of nlPoW with PoW. Preliminary results, simulating a network of 1000 miners with identical computational power, indicate that nlPoW reduce the number of hash computations, and therefore the energy consumption, required by Bitcoin mining. The findings are significant because nlPoW does not reduce the degree of decentralised consensus, or trade energy usage for some other resource as is the case with many other attempts to address the energy consumption problem in PoW.

Abstract 550 | PDF Downloads 423

Similar Articles

41-50 of 54

You may also start an advanced similarity search for this article.