IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i19p6384-d650624.html
   My bibliography  Save this article

Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review

Author

Listed:
  • Nasser Kimbugwe

    (School of Computer Science, Xiangtan University, Xiangtan 411105, China
    Department of Networks, College of Computing & I.S, Makerere University, Kampala 7062, Uganda)

  • Tingrui Pei

    (School of Computer Science, Xiangtan University, Xiangtan 411105, China
    Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan 411105, China)

  • Moses Ntanda Kyebambe

    (Department of Networks, College of Computing & I.S, Makerere University, Kampala 7062, Uganda)

Abstract

The role of the Internet of Things (IoT) networks and systems in our daily life cannot be underestimated. IoT is among the fastest evolving innovative technologies that are digitizing and interconnecting many domains. Most life-critical and finance-critical systems are now IoT-based. It is, therefore, paramount that the Quality of Service (QoS) of IoTs is guaranteed. Traditionally, IoTs use heuristic, game theory approaches and optimization techniques for QoS guarantee. However, these methods and approaches have challenges whenever the number of users and devices increases or when multicellular situations are considered. Moreover, IoTs receive and generate huge amounts of data that cannot be effectively handled by the traditional methods for QoS assurance, especially in extracting useful features from this data. Deep Learning (DL) approaches have been suggested as a potential candidate in solving and handling the above-mentioned challenges in order to enhance and guarantee QoS in IoT. In this paper, we provide an extensive review of how DL techniques have been applied to enhance QoS in IoT. From the papers reviewed, we note that QoS in IoT-based systems is breached when the security and privacy of the systems are compromised or when the IoT resources are not properly managed. Therefore, this paper aims at finding out how Deep Learning has been applied to enhance QoS in IoT by preventing security and privacy breaches of the IoT-based systems and ensuring the proper and efficient allocation and management of IoT resources. We identify Deep Learning models and technologies described in state-of-the-art research and review papers and identify those that are most used in handling IoT QoS issues. We provide a detailed explanation of QoS in IoT and an overview of commonly used DL-based algorithms in enhancing QoS. Then, we provide a comprehensive discussion of how various DL techniques have been applied for enhancing QoS. We conclude the paper by highlighting the emerging areas of research around Deep Learning and its applicability in IoT QoS enhancement, future trends, and the associated challenges in the application of Deep Learning for QoS in IoT.

Suggested Citation

  • Nasser Kimbugwe & Tingrui Pei & Moses Ntanda Kyebambe, 2021. "Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review," Energies, MDPI, vol. 14(19), pages 1-27, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6384-:d:650624
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/19/6384/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/19/6384/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fotios Zantalis & Grigorios Koulouras & Sotiris Karabetsos & Dionisis Kandris, 2019. "A Review of Machine Learning and IoT in Smart Transportation," Future Internet, MDPI, vol. 11(4), pages 1-23, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Konstantinos Ntafloukas & Liliana Pasquale & Beatriz Martinez-Pastor & Daniel P. McCrum, 2023. "A Vulnerability Assessment Approach for Transportation Networks Subjected to Cyber–Physical Attacks," Future Internet, MDPI, vol. 15(3), pages 1-23, February.
    2. Wu, Chao, 2024. "Data privacy: From transparency to fairness," Technology in Society, Elsevier, vol. 76(C).
    3. Heidary Dahooie, Jalil & Mohammadian, Ayoub & Qorbani, Ali Reza & Daim, Tugrul, 2023. "A portfolio selection of internet of things (IoTs) applications for the sustainable urban transportation: A novel hybrid multi criteria decision making approach," Technology in Society, Elsevier, vol. 75(C).
    4. Nadine Bachmann & Shailesh Tripathi & Manuel Brunner & Herbert Jodlbauer, 2022. "The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals," Sustainability, MDPI, vol. 14(5), pages 1-33, February.
    5. Abderahman Rejeb & John G. Keogh & Horst Treiblmaier, 2019. "Leveraging the Internet of Things and Blockchain Technology in Supply Chain Management," Future Internet, MDPI, vol. 11(7), pages 1-22, July.
    6. Zhang, Wuxia & Wu, Yupeng & Calautit, John Kaiser, 2022. "A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    7. Haqi Khalid & Shaiful Jahari Hashim & Sharifah Mumtazah Syed Ahmad & Fazirulhisyam Hashim & Muhammad Akmal Chaudhary, 2021. "A New Hybrid Online and Offline Multi-Factor Cross-Domain Authentication Method for IoT Applications in the Automotive Industry," Energies, MDPI, vol. 14(21), pages 1-34, November.
    8. Khadijeh Alibabaei & Eduardo Assunção & Pedro D. Gaspar & Vasco N. G. J. Soares & João M. L. P. Caldeira, 2022. "Real-Time Detection of Vine Trunk for Robot Localization Using Deep Learning Models Developed for Edge TPU Devices," Future Internet, MDPI, vol. 14(7), pages 1-16, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6384-:d:650624. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.