Optimization of the Traceability of Perishable Products Through Light Blockchain and IoT in the Food Industry
Traceability in the supply chain of perishable products faces significant challenges due to the limitations of traditional systems, such as centralized databases and conventional blockchain technologies. These solutions are often insufficient to ensure fast response times, energy efficiency, and sca...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10966924/ |
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| Summary: | Traceability in the supply chain of perishable products faces significant challenges due to the limitations of traditional systems, such as centralized databases and conventional blockchain technologies. These solutions are often insufficient to ensure fast response times, energy efficiency, and scalability in dynamic scenarios, negatively impacting the quality and safety of food products. Furthermore, compliance with international regulations, such as those of the FDA and the European Union, requires implementing more efficient and transparent systems. This work proposes an innovative system that combines lightweight blockchain with IoT devices for traceability in the food industry. The system architecture is based on blockchain nodes optimized using lightweight consensus mechanisms, such as Proof of Authority, and integration with IoT sensors through efficient protocols such as MQTT. This combination ensures the collection and immutable recording of real-time data, including critical parameters such as temperature and location. The results show that the system reduces average response times to 105 ms in configurations with 10 nodes and 50 sensors, achieving transaction rates per second of up to 720. It also features an average energy consumption of 9.6 mJ per transaction and failure recovery times of just 5 seconds. These metrics highlight the system’s ability to operate in demanding environments with high data density, significantly improving existing technologies. |
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| ISSN: | 2169-3536 |