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Deep Learning in IoT systems: A Review

Author

Listed:
  • 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.)

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.

Suggested Citation

  • Shavan Askar & Chnar Mustaf Mohammed & Shahab Wahhab Kareem, 2021. "Deep Learning in IoT systems: A Review," International Journal of Science and Business, IJSAB International, vol. 5(6), pages 131-147.
  • Handle: RePEc:aif:journl:v:5:y:2021:i:6:p:131-147
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    References listed on IDEAS

    as
    1. Chnar Mustaf Mohammed & Shavan Askar, 2021. "Machine Learning for IoT HealthCare Applications: A Review," International Journal of Science and Business, IJSAB International, vol. 5(3), pages 42-51.
    2. Glena Aziz Qadir & Shavan Askar, 2021. "Software Defined Network Based VANET," International Journal of Science and Business, IJSAB International, vol. 5(3), pages 83-91.
    3. Zhwan Mohammed Khalid & Shavan Askar, 2021. "Resistant Blockchain Cryptography to Quantum Computing Attacks," International Journal of Science and Business, IJSAB International, vol. 5(3), pages 116-125.
    4. Baydaa Hassan Husain & Shavan Askar, 2021. "Survey on Edge Computing Security," International Journal of Science and Business, IJSAB International, vol. 5(3), pages 52-60.
    5. Kosrat Dlshad Ahmed & Shavan Askar, 2021. "Deep Learning Models for Cyber Security in IoT Networks: A Review," International Journal of Science and Business, IJSAB International, vol. 5(3), pages 61-70.
    6. Zhala Jameel Hamad & Shavan Askar, 2021. "Machine Learning Powered IoT for Smart Applications," International Journal of Science and Business, IJSAB International, vol. 5(3), pages 92-100.
    7. Ibrahim Shamal Abdulkhaleq & Shavan Askar, 2021. "Evaluating the Impact of Network Latency on the Safety of Blockchain Transactions," International Journal of Science and Business, IJSAB International, vol. 5(3), pages 71-82.
    8. Kurdistan Ali & Shavan Askar, 2021. "Security Issues and Vulnerability of IoT Devices," International Journal of Science and Business, IJSAB International, vol. 5(3), pages 101-115.
    9. Shavan Askar & Glena Aziz Qadir & Tarik A. Rashid, 2021. "SDN Based 5G VANET: A Review," International Journal of Science and Business, IJSAB International, vol. 5(6), pages 131-147.
    Full references (including those not matched with items on IDEAS)

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