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A Systematic Review of Consensus Mechanisms in Blockchain

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  • Sisi Zhou

    (School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
    Hunan Key Laboratory for Service Computing and Novel Software Technology, Xiangtan 411201, China)

  • Kuanching Li

    (School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
    Hunan Key Laboratory for Service Computing and Novel Software Technology, Xiangtan 411201, China)

  • Lijun Xiao

    (School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
    Hunan Key Laboratory for Service Computing and Novel Software Technology, Xiangtan 411201, China)

  • Jiahong Cai

    (School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
    Hunan Key Laboratory for Service Computing and Novel Software Technology, Xiangtan 411201, China)

  • Wei Liang

    (School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
    Hunan Key Laboratory for Service Computing and Novel Software Technology, Xiangtan 411201, China)

  • Arcangelo Castiglione

    (Department of Computer Science, Università degli Studi di Salerno, 84084 Fisciano, Italy)

Abstract

Since the birth of Bitcoin, blockchain has shifted from a critical cryptocurrency technology to an enabling technology. Due to its immutability and trustworthiness, blockchain has revolutionized many fields requiring credibility and high-quality data for decision making. Particularly in business intelligence and business process management, users can use blockchain to build their blockchain-enabled collaboration and data-sharing ecosystem with their partners. In this paper, we present the development process of blockchain and consensus mechanisms, where important blockchain consensus mechanisms are introduced. The consensus mechanism is the kernel among various blockchain components to ensure security and performance. Again, we present a comparison of these consensus mechanisms from different perspectives. We take the blockchain-enabling business as an example and analyze the relationship between blockchain and business process characteristics and the ideas and principles for selecting consensus mechanisms. Finally, we describe the differences and connections among many consensus mechanisms while laying a foundation for selecting appropriate consensus mechanisms for different scenarios and fields of application.

Suggested Citation

  • Sisi Zhou & Kuanching Li & Lijun Xiao & Jiahong Cai & Wei Liang & Arcangelo Castiglione, 2023. "A Systematic Review of Consensus Mechanisms in Blockchain," Mathematics, MDPI, vol. 11(10), pages 1-27, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2248-:d:1144294
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    References listed on IDEAS

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    1. Yaçine Merrad & Mohamed Hadi Habaebi & Elfatih A. A. Elsheikh & Fakher Eldin. M. Suliman & Md Rafiqul Islam & Teddy Surya Gunawan & Mokhtaria Mesri, 2022. "Blockchain: Consensus Algorithm Key Performance Indicators, Trade-Offs, Current Trends, Common Drawbacks, and Novel Solution Proposals," Mathematics, MDPI, vol. 10(15), pages 1-35, August.
    2. Tu, Zhiyong & Xue, Changyong, 2019. "Effect of bifurcation on the interaction between Bitcoin and Litecoin," Finance Research Letters, Elsevier, vol. 31(C).
    3. Alexandre Dolgui & Dmitry Ivanov & Boris Sokolov, 2018. "Ripple effect in the supply chain: an analysis and recent literature," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 414-430, January.
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    Cited by:

    1. Casey Watters, 2023. "Digital Gold or Digital Security? Unravelling the Legal Fabric of Decentralised Digital Assets," Commodities, MDPI, vol. 2(4), pages 1-12, October.
    2. Dilan C. Hangawatta & Ameen Gargoom & Abbas Z. Kouzani, 2024. "Machine-Learning-Driven Identification of Electrical Phases in Low-Sampling-Rate Consumer Data," Energies, MDPI, vol. 18(1), pages 1-21, December.
    3. Elias Dritsas & Maria Trigka, 2024. "Machine Learning for Blockchain and IoT Systems in Smart Cities: A Survey," Future Internet, MDPI, vol. 16(9), pages 1-15, September.

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