IoT, Blockchain, Big Data and Artificial Intelligence (IBBA) Framework—For Real-Time Food Safety Monitoring
Technological advancements in mechanized food production have expanded markets beyond geographical boundaries. At the same time, the risk of contamination has increased severalfold, often resulting in significant damage in terms of food wastage, economic loss to the producers, danger to public healt...
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/105 |
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| author | Siva Peddareddigari Sri Vigna Hema Vijayan Manickavasagan Annamalai |
| author_facet | Siva Peddareddigari Sri Vigna Hema Vijayan Manickavasagan Annamalai |
| author_sort | Siva Peddareddigari |
| collection | DOAJ |
| description | Technological advancements in mechanized food production have expanded markets beyond geographical boundaries. At the same time, the risk of contamination has increased severalfold, often resulting in significant damage in terms of food wastage, economic loss to the producers, danger to public health, or all of these. In general, governments across the world have recognized the importance of having food safety processes in place to impose food recalls as required. However, the primary challenges to the existing practices are delays in identifying unsafe food, siloed data handling, delayed decision making, and tracing the source of contamination. Leveraging the Internet of Things (IoT), 5G, blockchains, cloud computing, and big data, a novel framework has been proposed to address the current challenges. The framework enables real-time data gathering and in situ application of machine learning-powered algorithms to predict contamination and facilitate instant decision making. Since the data are processed in real time, the proposed approach enables contamination to be identified early and informed decisions to be made confidently, thereby helping to reduce damage significantly. The proposed approach also throws up new challenges in terms of the implementation of changes to data collection across all phases of food production, onboarding various stockholders, and adaptation to a new process. |
| format | Article |
| id | doaj-art-460fc4e5c0354246a03c10c47b5552e6 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-460fc4e5c0354246a03c10c47b5552e62025-01-10T13:14:27ZengMDPI AGApplied Sciences2076-34172024-12-0115110510.3390/app15010105IoT, Blockchain, Big Data and Artificial Intelligence (IBBA) Framework—For Real-Time Food Safety MonitoringSiva Peddareddigari0Sri Vigna Hema Vijayan1Manickavasagan Annamalai2School of Engineering, University of Guelph, Guelph, ON N1G 2W1, CanadaSchool of Engineering, University of Guelph, Guelph, ON N1G 2W1, CanadaSchool of Engineering, University of Guelph, Guelph, ON N1G 2W1, CanadaTechnological advancements in mechanized food production have expanded markets beyond geographical boundaries. At the same time, the risk of contamination has increased severalfold, often resulting in significant damage in terms of food wastage, economic loss to the producers, danger to public health, or all of these. In general, governments across the world have recognized the importance of having food safety processes in place to impose food recalls as required. However, the primary challenges to the existing practices are delays in identifying unsafe food, siloed data handling, delayed decision making, and tracing the source of contamination. Leveraging the Internet of Things (IoT), 5G, blockchains, cloud computing, and big data, a novel framework has been proposed to address the current challenges. The framework enables real-time data gathering and in situ application of machine learning-powered algorithms to predict contamination and facilitate instant decision making. Since the data are processed in real time, the proposed approach enables contamination to be identified early and informed decisions to be made confidently, thereby helping to reduce damage significantly. The proposed approach also throws up new challenges in terms of the implementation of changes to data collection across all phases of food production, onboarding various stockholders, and adaptation to a new process.https://www.mdpi.com/2076-3417/15/1/105food safetyfood recallInternet of Things (IoT)machine learning (ML)blockchainbig data |
| spellingShingle | Siva Peddareddigari Sri Vigna Hema Vijayan Manickavasagan Annamalai IoT, Blockchain, Big Data and Artificial Intelligence (IBBA) Framework—For Real-Time Food Safety Monitoring Applied Sciences food safety food recall Internet of Things (IoT) machine learning (ML) blockchain big data |
| title | IoT, Blockchain, Big Data and Artificial Intelligence (IBBA) Framework—For Real-Time Food Safety Monitoring |
| title_full | IoT, Blockchain, Big Data and Artificial Intelligence (IBBA) Framework—For Real-Time Food Safety Monitoring |
| title_fullStr | IoT, Blockchain, Big Data and Artificial Intelligence (IBBA) Framework—For Real-Time Food Safety Monitoring |
| title_full_unstemmed | IoT, Blockchain, Big Data and Artificial Intelligence (IBBA) Framework—For Real-Time Food Safety Monitoring |
| title_short | IoT, Blockchain, Big Data and Artificial Intelligence (IBBA) Framework—For Real-Time Food Safety Monitoring |
| title_sort | iot blockchain big data and artificial intelligence ibba framework for real time food safety monitoring |
| topic | food safety food recall Internet of Things (IoT) machine learning (ML) blockchain big data |
| url | https://www.mdpi.com/2076-3417/15/1/105 |
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