Author(s): Kadiri Kamoru Oluwatoyin, Abubakar Dauda and Enem Theophilus Aniemeka

Abstract: The fast growth in the number of smart devices capable of running complex apps significantly impacts the information communication technology industry's landscape. The Internet of Things (IoT) continues to grow in popularity and relevance in man's daily existence. However, as the Internet of Things evolves, so do the associated problems. Thus, the need for IoT development and ongoing upgrading becomes stronger. To maximize the potential of IoT systems, machine learning technologies have recently been used. The implementation of machine learning algorithms in IoT systems is examined in detail in this paper. Two categories of machine learning-based IoT algorithms deal with fundamental IoT challenges like localization, clustering, routing, and data aggregation. Additional machine learning-based IoT algorithms deal with performance challenges like congestion control, fault detection, resource management, and security.

DOI: 10.33545/27076571.2021.v2.i1a.28

Pages: 62-68 | Views: 629 | Downloads: 300

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