Deep Learning in IoT systems: A Review

Shavan Askar, Chnar Mustaf Mohammed and Shahab Wahhab Kareem
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Shavan Askar: Assistant Professor, CEO of Arcella Telecom, College of Engineering, Erbil Polytechnic University, Erbil, Iraq.
Chnar Mustaf Mohammed: Information System Engineering, Erbil Polytechnic University, Erbil, Iraq.
Shahab Wahhab Kareem: Lecturer, Erbil Polytechnic University, Erbil, Iraq.

International Journal of Science and Business, 2021, vol. 5, issue 6, 131-147

Abstract: The expansion of the internet, along with its interconnection of devices has made it possible to increase the world's interconnectedness in these days, with the growth in internet connectivity capabilities and quality, a lot of items are interconnected, which means they communicate with each other using new and powerful techniques. Innovative sensor systems are spreading their consumers are strongly connected to the internet. The growth of linked sensors and systems has an incremental impact on the quantity of data. Regardless of its purpose, it is accumulating whole data. The Internet of Things (IoT) has a practical use for industries such as obtaining field data, tracking it and keeping them, all connected. To imitate the human intelligence level, the machine or software is made smarter by using advanced deep learning. In the paper, several diverse types of IoT technologies will be referenced, including intelligent cities, smart health care, mobility networks, and educational systems, among others. In addition, a range of novel deep learning algorithms that were implemented to simplify the intelligent usage of the machines without involving human control has been reviewed and good results of each algorithm in different categories are demonstrated as a table of comparison. This paper gives an overview of the applications that need to combine deep learning to serve IoT applications in an efficient and automated manner.

Keywords: Internet of Things (IoT); Deep Learning (DL); IoT Applications; Challenges; DL Algorithms; DL Platforms (search for similar items in EconPapers)
Date: 2021
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