SmartHerd management : A microservices-based fog computing-assisted IoT platform towards data-driven smart dairy farming
Internet of Things (IoT), fog computing, cloud computing, and data-driven techniques together offer a great opportunity for verticals such as dairy industry to increase productivity by getting actionable insights to improve farming practices, thereby increasing efficiency and yield. In this paper, w...
| Veröffentlicht in: | Software: practice & experience. - 1998. - 49(2019), 7 vom: 15. Juli, Seite 1055-1078 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2019
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| Zugriff auf das übergeordnete Werk: | Software: practice & experience |
| Schlagworte: | Journal Article Internet of Things (IoT) cloud computing dairy farming data analytics data‐driven fog computing microservices smart farm |
| Zusammenfassung: | Internet of Things (IoT), fog computing, cloud computing, and data-driven techniques together offer a great opportunity for verticals such as dairy industry to increase productivity by getting actionable insights to improve farming practices, thereby increasing efficiency and yield. In this paper, we present SmartHerd, a fog computing-assisted end-to-end IoT platform for animal behavior analysis and health monitoring in a dairy farming scenario. The platform follows a microservices-oriented design to assist the distributed computing paradigm and addresses the major issue of constrained Internet connectivity in remote farm locations. We present the implementation of the designed software system in a 6-month mature real-world deployment, wherein the data from wearables on cows is sent to a fog-based platform for data classification and analysis, which includes decision-making capabilities and provides actionable insights to farmer towards the welfare of animals. With fog-based computational assistance in the SmartHerd setup, we see an 84% reduction in amount of data transferred to the cloud as compared with the conventional cloud-based approach |
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| Beschreibung: | Date Revised 13.10.2023 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 0038-0644 |
| DOI: | 10.1002/spe.2704 |