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| Format: | Recurso digital |
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Zenodo
2024
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| Online Access: | https://doi.org/10.5281/zenodo.14423571 |
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| author | Sabarna Choudhuri Abhishek Gupta |
| author_facet | Sabarna Choudhuri Abhishek Gupta |
| contents | <p><span>The rapid proliferation of Internet of Things (IoT) devices has resulted in unprecedented volumes of real-time data, necessitating advanced solutions for efficient processing and analytics. This research explores the integration of Artificial Intelligence (AI) with Cloud Engineering to address these challenges, leveraging the predictive capabilities of AI and the scalability of cloud platforms. The study evaluates system performance, AI model accuracy, data pipeline efficiency, and energy consumption across various cloud platforms and real-world use cases. Results demonstrate that hybrid architectures combining edge and cloud processing significantly reduce latency, improve accuracy, and optimize operational efficiency. Use cases such as healthcare monitoring, smart city management, and industrial automation validate the practical applicability of the proposed framework. This integration not only enhances IoT ecosystem performance but also aligns with sustainability goals through energy-efficient operations. The findings provide a roadmap for deploying scalable, intelligent, and cost-effective IoT systems, while identifying future directions in 5G, quantum computing, and regulatory compliance for improved implementation</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14423571 |
| institution | Zenodo |
| language | |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Integrating AI with Cloud Engineering for Real-Time Data Processing and Analytics in IoT Applications Sabarna Choudhuri Abhishek Gupta <p><span>The rapid proliferation of Internet of Things (IoT) devices has resulted in unprecedented volumes of real-time data, necessitating advanced solutions for efficient processing and analytics. This research explores the integration of Artificial Intelligence (AI) with Cloud Engineering to address these challenges, leveraging the predictive capabilities of AI and the scalability of cloud platforms. The study evaluates system performance, AI model accuracy, data pipeline efficiency, and energy consumption across various cloud platforms and real-world use cases. Results demonstrate that hybrid architectures combining edge and cloud processing significantly reduce latency, improve accuracy, and optimize operational efficiency. Use cases such as healthcare monitoring, smart city management, and industrial automation validate the practical applicability of the proposed framework. This integration not only enhances IoT ecosystem performance but also aligns with sustainability goals through energy-efficient operations. The findings provide a roadmap for deploying scalable, intelligent, and cost-effective IoT systems, while identifying future directions in 5G, quantum computing, and regulatory compliance for improved implementation</span></p> |
| title | Integrating AI with Cloud Engineering for Real-Time Data Processing and Analytics in IoT Applications |
| url | https://doi.org/10.5281/zenodo.14423571 |