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Analysis of Machine Learning Algorithms for Efficient Cloud and Edge Computing in the IoT

Analysis of Machine Learning Algorithms for Efficient Cloud and Edge Computing in the IoT

Ramya R., Ramamoorthy S.
Copyright: © 2022 |Pages: 19
ISBN13: 9781668438046|ISBN10: 1668438046|ISBN13 Softcover: 9781668438053|EISBN13: 9781668438060
DOI: 10.4018/978-1-6684-3804-6.ch006
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MLA

R., Ramya, and Ramamoorthy S. "Analysis of Machine Learning Algorithms for Efficient Cloud and Edge Computing in the IoT." Challenges and Risks Involved in Deploying 6G and NextGen Networks, edited by A.M. Viswa Bharathy and Basim Alhadidi, IGI Global, 2022, pp. 72-90. https://doi.org/10.4018/978-1-6684-3804-6.ch006

APA

R., R. & S., R. (2022). Analysis of Machine Learning Algorithms for Efficient Cloud and Edge Computing in the IoT. In A. Bharathy & B. Alhadidi (Eds.), Challenges and Risks Involved in Deploying 6G and NextGen Networks (pp. 72-90). IGI Global. https://doi.org/10.4018/978-1-6684-3804-6.ch006

Chicago

R., Ramya, and Ramamoorthy S. "Analysis of Machine Learning Algorithms for Efficient Cloud and Edge Computing in the IoT." In Challenges and Risks Involved in Deploying 6G and NextGen Networks, edited by A.M. Viswa Bharathy and Basim Alhadidi, 72-90. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-3804-6.ch006

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Abstract

The internet of things (IoT) technology, which connects internet-connected devices, continues to extend the current internet by allowing communication and interactions across the physical and cyber worlds. The IoT generates big data that is characterized by its velocity in terms of time and place dependency, with a diversity of diverse modalities and fluctuating data quality, in addition to increased volume. The key to designing smart IoT applications is intelligent data processing and analysis. This chapter first describes the details of how cloud services and edge technology work and support the internet of things with many challenges and limitations in the overall internet services. Second, it describes the support of different machine learning algorithms (MLA) in the different fields of internet of things applications. Finally, there is a description of the future research scopes and open issues in the field of the internet of things with machine learning algorithms for further research work initiation.

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