4.6 Article

Blockchain-Driven IoT for Food Traceability With an Integrated Consensus Mechanism

期刊

IEEE ACCESS
卷 7, 期 -, 页码 129000-129017

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2940227

关键词

Food traceability; blockchain; consensus mechanism; Internet of Things; shelf life management

资金

  1. Research Office of the Hong Kong Polytechnic University
  2. Blockchain Group in PRISC
  3. Department of Supply Chain and Information Management of The Hang Seng University of Hong Kong
  4. IBM China/HK Limited under Academic Initiative Program

向作者/读者索取更多资源

Food traceability has been one of the emerging blockchain applications in recent years, for improving the areas of anti-counterfeiting and quality assurance. Existing food traceability systems do not guarantee a high level of system reliability, scalability, and information accuracy. Moreover, the traceability process is time-consuming and complicated in modern supply chain networks. To alleviate these concerns, blockchain technology is promising to create a new ontology for supply chain traceability. However, most consensus mechanisms and data flow in blockchain are developed for cryptocurrency, not for supply chain traceability; hence, simply applying blockchain technology to food traceability is impractical. In this paper, a blockchain-IoT-based food traceability system (BIFTS) is proposed to integrate the novel deployment of blockchain, IoT technology, and fuzzy logic into a total traceability shelf life management system for managing perishable food. To address the needs for food traceability, lightweight and vaporized characteristics are deployed in the blockchain, while an integrated consensus mechanism that considers shipment transit time, stakeholder assessment, and shipment volume is developed. The data flow of blockchain is then aligned to the deployment of IoT technologies according to the level of traceable resource units. Subsequently, the decision support can be established in the food supply chain by using reliable and accurate data for shelf life adjustment, and by using fuzzy logic for quality decay evaluation.

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