Abstract
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.
Highlights
Internet of Things (IoT) is the connectivity of the enormous amount of physical devices to the internet to collect, share, and analyze massive chunks of data
The research papers selected for this study mostly focused on IoT, machine learning techniques to analyze SDN network traffic, and techniques for IoT leveraging SDN
SDN aims 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
Summary
Internet of Things (IoT) is the connectivity of the enormous amount of physical devices to the internet to collect, share, and analyze massive chunks of data. A survey conducted by Cisco forecasts that 50 billion things will be interconnected through the internet [3] Interconnection of such a huge number of devices leads to management and scalability issues. IoT connects physical devices and virtual objects through communication protocols such as Bluetooth low energy (BLE), WiFi, ZigBee, Z-Wave, Long Range Wide Area Network (LoRaWAN), to name a few. These IoT devices have dynamic configuration and remotely accessible interfaces [74]. Recent development and contributions in the IoT field introduced new concepts and IoT terms such as machine-to-machine (M2M), industrial IoT (IIoT), Internet of everything (IoE), Internet of anything (IoA), social IoT (SIoT), and web of things (WoT)
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