International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Optimizing IoT Data Ingestion Pipelines with Apache NiFi for Real-Time Monitoring in Smart Food Safety Management

Author(s) Urvangkumar Kothari
Country United States
Abstract Application of Internet of Things (IoT) solutions to the systems of food safety management have made it possible to monitor the environmental conditions at the food supply chain continuously and in real-time. Nonetheless, the consumption of large and diverse sensor data poses great issues related to latency, data integrity and scalability. In this paper, an optimized architecture of the data ingestion pipeline in the Internet of Things (IoT) is suggested with the use of Apache NiFi into real-time monitoring of smart food safety management. Apache NiFi also uses its powerful features such as visual flow-based programming, data provenance, and dynamic prioritization capabilities to simplify data flow, enhance data fault tolerance, and generate fruitful data transformation. The suggested system architecture deals with the essential ingestion issues and proves how scalable, low-latency data pipelines may facilitate in a timely fashion decision-making and compliance in food safety settings. An effective implementation plan and optimization measures have also been described in the paper which gives a blue print to any future industrial application in the food industry.
Keywords IoT, Apache NiFi, Food Safety, Data Ingestion, Real-time Monitoring, Smart agriculture, Data pipeline, Edge Processing, Sensor networks, Supply Chain Management.
Field Engineering
Published In Volume 2, Issue 6, November-December 2020
Published On 2020-12-04
DOI https://doi.org/10.36948/ijfmr.2020.v02i06.51991
Short DOI https://doi.org/g9tzrs
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