Perspectives of Machine Learning and Deep Learning in Internet of Things and Cloud

Abstract

For centuries, the concept of a smart, autonomous learning machine has fascinated people. The machine learning philosophy is to automate the development of analytical models so that algorithms can learn continually with the assistance of accessible information. Machine learning (ML) and deep learning (DL) methods are implemented to further improve an application's intelligence and capacities as the quantity of the gathered information rises. Because IoT will be one of the main sources of information, data science will make a significant contribution to making IoT apps smarter. There is a rapid development of both technologies, cloud computing and the internet of things, considering the field of wireless communication. This chapter answers the questions: How can IoT intelligent information be applied to ML and DL algorithms? What is the taxonomy of IoT's ML and DL and profound learning algorithms? And what are real-world IoT data features that require data analytics?

A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges

“…Things with constrained resources are connected to IoT to generate enormous data, and cloud-based applications will be used to process the data. This data shift from constrained things to a cloud-based collaborative environment will result in a cognitive world [110].…”