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Modeling and Analysis of Proof-Based Strategies for Distributed Consensus in Blockchain-Based Peer-to-Peer Networks

Author

Listed:
  • Majed Abdullah Alrowaily

    (Department of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia)

  • Mansoor Alghamdi

    (Department of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia)

  • Ibrahim Alkhazi

    (College of Computers & Information Technology, University of Tabuk, Tabuk 47512, Saudi Arabia)

  • Ahmad B. Hassanat

    (Faculty of Information Technology, Mutah University, Karak 61710, Jordan)

  • Musab Mutasim Saeed Arbab

    (Department of Administrative Sciences, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia)

  • Charles Z. Liu

    (School of Electrical and Information Engineering, University of Sydney, Sydney, NSW 2008, Australia)

Abstract

Blockchain technology has a wide range of applicability in the fields of transportation infrastructure construction and maintenance, transportation big data analysis and application, expressway toll collection, and logistics. The core technology lies in the distributed, decentralized, immutable, and programmable features brought about by consensus. This paper studies the dynamic analytical modeling of Proof-Based Consensus (PBC) strategies in blockchain systems, focusing on basic strategies, including Proof of Work (PoW), Proof of Stake (PoS), Proof of Authority (PoA), and Proof of Luck (PoL), which can be extended to other PBC models. We focus on modeling these typical strategies and discuss their solution characteristics in terms of algorithmic mechanisms and principles. The relevant results can be used for quantitative analysis and evaluation of distributed consensus based on the model.

Suggested Citation

  • Majed Abdullah Alrowaily & Mansoor Alghamdi & Ibrahim Alkhazi & Ahmad B. Hassanat & Musab Mutasim Saeed Arbab & Charles Z. Liu, 2023. "Modeling and Analysis of Proof-Based Strategies for Distributed Consensus in Blockchain-Based Peer-to-Peer Networks," Sustainability, MDPI, vol. 15(2), pages 1-23, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1478-:d:1033605
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    Cited by:

    1. Amogh Shukla & Tapan Kumar Das & Sanjiban Sekhar Roy, 2023. "TRX Cryptocurrency Profit and Transaction Success Rate Prediction Using Whale Optimization-Based Ensemble Learning Framework," Mathematics, MDPI, vol. 11(11), pages 1-27, May.

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