AI-Enhanced Blockchain for Scalable IoT-Based Supply Chain

Mohamed Moetez Abdelhamid, Layth Sliman () and Raoudha Ben Djemaa
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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

Logistics, 2024, vol. 8, issue 4, 1-33

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.

Keywords: blockchain; artificial intelligence; swarm intelligence; block pruning; scalability (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2024
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