Edge-to-Cloud Intelligence: Enhancing IoT Devices with Machine Learning and Cloud Computing
Abstract
The rapid growth of the Internet of Things (IoT) has exponentially increased the number of connected devices that generate large amounts of data. Leveraging advanced technologies such as machine learning and cloud computing is key to generating meaningful insights and enabling intelligent decision-making. This article describes the concept of edge-to-cloud intelligence, which combines edge and cloud computing paradigms to improve the capabilities of IoT devices. Explore the benefits, challenges, and considerations of edge computing, machine learning, and cloud computing in the context of IoT. Additionally, we are exploring the integration of these technologies to create a smart and efficient IoT ecosystem. This paper highlights the critical role of edge computing in enabling real-time analysis and decision-making at the edge while leveraging the power of cloud resources for advanced analytics, scalability, and storage. We discuss various use cases and examples of edge-to-cloud intelligence implementation and address challenges related to scalability, security, and privacy. Finally, we consider new trends and future directions in this field. By leveraging intelligence from the edge to the cloud, IoT devices can realize their full potential, enabling innovative applications in areas such as healthcare, manufacturing, transportation, and smart cities, resulting in increased efficiency. , reliability, and user experience.
Keywords
Edge-to-Cloud Intelligence, Internet of Things (IoT), Machine Learning, Cloud Computing, Edge Computing, IoT
Citations
IRE Journals:
Satyanarayan Kanungo
"Edge-to-Cloud Intelligence: Enhancing IoT Devices with Machine Learning and Cloud Computing" Iconic Research And Engineering Journals Volume 2 Issue 12 2019 Page 238-245
IEEE:
Satyanarayan Kanungo
"Edge-to-Cloud Intelligence: Enhancing IoT Devices with Machine Learning and Cloud Computing" Iconic Research And Engineering Journals, 2(12)
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