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AI-Enhanced Blockchain for Scalable IoT-Based Supply Chain

Author

Listed:
  • Mohamed Moetez Abdelhamid

    (Miracl Lab, Higher Institute of Computer Science and Communication Techniques of Sousse, University of Sousse, Hammam Sousse 4011, Tunisia)

  • Layth Sliman

    (Efrei Research Lab, Panthéon-Assas University, 94800 Villejuif, France)

  • Raoudha Ben Djemaa

    (Miracl Lab, Higher Institute of Computer Science and Communication Techniques of Sousse, University of Sousse, Hammam Sousse 4011, Tunisia)

Abstract

Purpose : The integration of AI with blockchain technology is investigated in this study to address challenges in IoT-based supply chains, specifically focusing on latency, scalability, and data consistency. Background : Despite the potential of blockchain technology, its application in supply chains is hindered by significant limitations such as latency and scalability, which negatively impact data consistency and system reliability. Traditional solutions such as sharding, pruning, and off-chain storage introduce technical complexities and reduce transparency. Methods : This research proposes an AI-enabled blockchain solution, ABISChain, designed to enhance the performance of supply chains. The system utilizes beliefs, desires, and intentions (BDI) agents to manage and prune blockchain data, thus optimizing the blockchain’s performance. A particle swarm optimization method is employed to determine the most efficient dataset for pruning across the network. Results : The AI-driven ABISChain platform demonstrates improved scalability, data consistency, and security, making it a viable solution for supply chain management. Conclusions : The findings provide valuable insights for supply chain managers and technology developers, offering a robust solution that combines AI and blockchain to overcome existing challenges in IoT-based supply chains.

Suggested Citation

  • Mohamed Moetez Abdelhamid & Layth Sliman & Raoudha Ben Djemaa, 2024. "AI-Enhanced Blockchain for Scalable IoT-Based Supply Chain," Logistics, MDPI, vol. 8(4), pages 1-33, November.
    • Handle: RePEc:gam:jlogis:v:8:y:2024:i:4:p:109-:d:1513769
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