Optimizing pool mining performance: A VIKOR-based model for identifying reputed miners in blockchain networks

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Concurrency and Computation-Practice & Experience Pub Date : 2024-06-23 DOI:10.1002/cpe.8211
Naga Sravanthi Puppala, R. Manoharan
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引用次数: 0

Abstract

Blockchain networks continue to gain attraction in cutting-edge applications and mining within these networks has become increasingly popular. To get rewards, miners solve cryptographic puzzles and add new blocks to blockchain networks using the proof-of-work (PoW) consensus mechanism. Numerous miners opt to participate in mining pools due to the challenges of solo mining. However, selecting reputed miners for pool mining poses a significant challenge, given the decentralized nature of the blockchain system. This paper addresses this challenge by introducing a new ranking model that evaluates miners' performance and reputation through trust scores. It provides a method for optimizing pool mining performance by identifying highly reputed miners within mining pools, enhancing overall pool profitability. This endeavor necessitates the development of ranking algorithms tailored to the unique dynamics of mining pools. The research offers a meticulously designed ranking model that identifies reputed miners. We extensively evaluate the proposed model using the hyperledger blockchain framework, guaranteeing strong performance across vital metrics like block authorization time, Processing time, block creation time, validation time, and confirmation time.

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优化矿池挖矿性能:基于 VIKOR 的区块链网络知名矿工识别模式
区块链网络在尖端应用中不断获得吸引力,在这些网络中挖矿也变得越来越流行。为了获得奖励,矿工们利用工作量证明(PoW)共识机制解决加密谜题并向区块链网络添加新区块。由于单人挖矿的挑战,许多矿工选择加入矿池。然而,考虑到区块链系统的去中心化特性,为矿池挖矿挑选知名矿工是一项重大挑战。本文通过引入一种新的排名模型来解决这一难题,该模型通过信任分数来评估矿工的表现和声誉。它通过识别矿池中声誉卓著的矿工,提供了一种优化矿池挖矿性能的方法,从而提高了矿池的整体盈利能力。这项工作需要开发适合矿池独特动态的排名算法。本研究提供了一个精心设计的排名模型,可识别声誉卓著的矿工。我们使用超级账本区块链框架对所提出的模型进行了广泛评估,保证了在区块授权时间、处理时间、区块创建时间、验证时间和确认时间等重要指标上的强大性能。
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来源期刊
Concurrency and Computation-Practice & Experience
Concurrency and Computation-Practice & Experience 工程技术-计算机:理论方法
CiteScore
5.00
自引率
10.00%
发文量
664
审稿时长
9.6 months
期刊介绍: Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.
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