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Deep Learning and Fog Computing: A Review

Author

Listed:
  • Shavan Askar

    (Assistant Professor, CEO of Arcella Telecom, College of Engineering, Erbil Polytechnic University, Erbil, Iraq.)

  • Zhala Jameel Hamad

    (Information System Engineering, Erbil Polytechnic University, Erbil, Iraq.)

  • Shahab Wahhab Kareem

    (Lecturer, Erbil Polytechnic University, Erbil, Iraq.)

Abstract

Fog computing (FC) is a new architecture that aims to reduce network pressures throughout the core network as well as the cloud computing (CC) by bringing resource-intensive functions like computation, analytics, connectivity, also storage, nearest to the clients. In their operations, FC systems can make use of intelligence features to reap the benefits of data that is readily accessible with computing resources to be able to resolve the problem of excessive energy use with power for Internet-of-Things (IoT) apps that require speed. It generates large volumes of data, prompting the creation of a growing number of FC apps and services. Furthermore, Deep Learning (DL), an important field, has made significant progress in a variety of research areas, including robotics, face recognition, neuromorphic computing, decision-making, computer graphics, and speech recognition. Several studies have been suggested to look at how to use DL to solve FC issues. DL has become more common these days to improve FC apps as well as provide fog services such as security, resource management, accuracy, delay, and energy reduction, cost, data processing, and traffic modeling. The current review paper will focus on how to provide an overview of DL functions throughout the FC sector. The DL implementation for FC has evolved into powerful clients with services at the highest level, allowing for deeper analytics and mission answers that are more intelligent.

Suggested Citation

  • Shavan Askar & Zhala Jameel Hamad & Shahab Wahhab Kareem, 2021. "Deep Learning and Fog Computing: A Review," International Journal of Science and Business, IJSAB International, vol. 5(6), pages 197-208.
  • Handle: RePEc:aif:journl:v:5:y:2021:i:6:p:197-208
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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. Sergej Svorobej & Patricia Takako Endo & Malika Bendechache & Christos Filelis-Papadopoulos & Konstantinos M. Giannoutakis & George A. Gravvanis & Dimitrios Tzovaras & James Byrne & Theo Lynn, 2019. "Simulating Fog and Edge Computing Scenarios: An Overview and Research Challenges," Future Internet, MDPI, vol. 11(3), pages 1-15, February.
    8. 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.
    9. 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.
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