Integrating Cloud-Edge Architectures and Semantic Interoperability for Precision Agriculture: A Framework for Resilient Data Analytics and Resource Provisioning
Keywords:
Precision Agriculture, Cloud Computing, Internet of Things, Semantic WebAbstract
The rapid convergence of the Internet of Things (IoT), Cloud Computing, and Semantic Web technologies has catalyzed a paradigm shift in the management of specialty crops and natural resource allocation. This research explores the architectural integration of distributed sensing environments with centralized cloud-based analytics to optimize precision agriculture. By synthesizing the NIST cloud definition with Software-Defined Networking (SDN) and the Resource Description Framework (RDF), this study proposes a robust framework for managing high-volume agricultural data. The investigation focuses on the mechanisms of optimal resource provisioning, the role of extensible software architectures for data visualization, and the application of cloud services in mitigating the impacts of natural disasters through proactive resource allocation. Findings suggest that the adoption of Ruby on Rails and Amazon Web Services (AWS) provides a scalable foundation for real-time weather monitoring and predictive modeling. This article elaborates on the theoretical underpinnings of service-level agreements (SLAs) in Software-as-a-Service (SaaS) environments and the necessity of semantic schemas in ensuring data interoperability across disparate agricultural sensors. The study concludes that an integrated, cloud-native approach enhances both the economic viability of specialty crop production and the efficacy of emergency response strategies in the face of climatic volatility.
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