Indexation des mégadonnées basée IoT dans la chaîne d'approvisionnement alimentaire / Indexing Big Data Based on IoT in Food Supply Chain

Abstract

During the industrial revolution, ensuring food safety has become a critical concern, where the primary objective is to improve the quality of life and safety of citizens. This objective is challenged by the complexity of supply chain processes, which involve the collection of big data from multiple stages and actors, as well as inefficiencies in monitoring and traceability mechanisms that rely on technologies such as IoT and ICT. Alongside these technological factors, Blockchain technology has emerged as a promising solution, offering secure, tamper-proof, and transparent data management across supply chain stages. However, scalability issues hinder traceability query efficiency, as searches must be performed sequentially across blocks. This Ph.D thesis addresses this limitation by developing indexing techniques for big data in the food supply chain to optimize search processes and enhance traceability performance. The first contribution integrates the B-tree indexing technique with the blockchain by introducing a modified transaction structure that records fabrication time and computes per-block ranges. The B-tree structure manages these fabrication time ranges together with their corresponding block numbers, which are continuously updated with each new block until they cover the entire blockchain network. This approach enables the system to efficiently locate relevant blocks containing traceability data, allowing queries to be executed only within the identified blocks, thereby optimizing inter-block searches. The second contribution presents a novel blockchain-based traceability system that combines Natural Language Processing (NLP) with B+ tree indexing, where NLP interprets consumer text queries to identify target supply chain stages and the B+ tree index narrows searches to relevant blocks, ensuring accurate and efficient responses. The third contribution proposes the MerkleB+ tree (MB+ tree), a hybrid structure designed to optimize big data transaction management within each block, reducing linear scans while preserving the security guarantees of the Merkle tree. The simulation of these three systems was conducted using the Hyperledger Fabric framework, which supports food supply chain scenarios and enables the creation of a decentralized network based on our specifications. Performance testing under various network configurations showed that the three systems demonstrated significant improvements in query efficiency, providing faster and more reliable access to traceability information, as well as superior performance compared to existing methods