Next Article in Journal
Power Conversion Using Analytical Model of Cockcroft–Walton Voltage Multiplier Rectenna
Previous Article in Journal
ICEr: An Intermittent Computing Environment Based on a Run-Time Module for Energy-Harvesting IoT Devices with NVRAM
Review

A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges

1
Department of Computer Science, Bahria University, Islamabad 44000, Pakistan
2
Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
3
Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah 21493, Saudi Arabia
4
Department of Computer Science, University of Central Asia, 310 Lenin Street, Naryn 722918, Kyrgyzstan
5
Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 21493, Saudi Arabia
6
Department of Software, Korea National University of Transportation, Chungju 27469, Korea
7
Department of Biomedical Engineering, Korea National University of Transportation, Chungju 27469, Korea
8
Department of AI Robotics Engineering, Korea National University of Transportation, Chungju 27469, Korea
9
Department of IT & Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju 27469, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Habib Mostafaei
Electronics 2021, 10(8), 880; https://doi.org/10.3390/electronics10080880
Received: 8 February 2021 / Revised: 25 March 2021 / Accepted: 25 March 2021 / Published: 7 April 2021
(This article belongs to the Special Issue Applications of Software Defined Networking)
In recent years, rapid development has been made to the Internet of Things communication technologies, infrastructure, and physical resources management. These developments and research trends address challenges such as heterogeneous communication, quality of service requirements, unpredictable network conditions, and a massive influx of data. One major contribution to the research world is in the form of software-defined networking applications, which aim to deploy rule-based management to control and add intelligence to the network using high-level policies to have integral control of the network without knowing issues related to low-level configurations. Machine learning techniques coupled with software-defined networking can make the networking decision more intelligent and robust. The Internet of Things application has recently adopted virtualization of resources and network control with software-defined networking policies to make the traffic more controlled and maintainable. However, the requirements of software-defined networking and the Internet of Things must be aligned to make the adaptations possible. This paper aims to discuss the possible ways to make software-defined networking enabled Internet of Things application and discusses the challenges solved using the Internet of Things leveraging the software-defined network. We provide a topical survey of the application and impact of software-defined networking on the Internet of things networks. We also study the impact of machine learning techniques applied to software-defined networking and its application perspective. The study is carried out from the different perspectives of software-based Internet of Things networks, including wide-area networks, edge networks, and access networks. Machine learning techniques are presented from the perspective of network resources management, security, classification of traffic, quality of experience, and quality of service prediction. Finally, we discuss challenges and issues in adopting machine learning and software-defined networking for the Internet of Things applications. View Full-Text
Keywords: SDN; machine learning; IoT; SDN leveraging ML; IoT leveraging SDN; topical review SDN; machine learning; IoT; SDN leveraging ML; IoT leveraging SDN; topical review
Show Figures

Figure 1

MDPI and ACS Style

Imran; Ghaffar, Z.; Alshahrani, A.; Fayaz, M.; Alghamdi, A.M.; Gwak, J. A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges. Electronics 2021, 10, 880. https://doi.org/10.3390/electronics10080880

AMA Style

Imran, Ghaffar Z, Alshahrani A, Fayaz M, Alghamdi AM, Gwak J. A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges. Electronics. 2021; 10(8):880. https://doi.org/10.3390/electronics10080880

Chicago/Turabian Style

Imran, Zeba Ghaffar, Abdullah Alshahrani, Muhammad Fayaz, Ahmed M. Alghamdi, and Jeonghwan Gwak. 2021. "A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges" Electronics 10, no. 8: 880. https://doi.org/10.3390/electronics10080880

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop