## 7 Limitations and Future Research Directions  
This section lists the limitation of current IoT platforms, such as lack of interoperability, compatibility, realizability, and security. Proposing absolute SDN-based solutions is not realistic; however, machine learning applied to SDN controllers will lead to IoT's realization. Figure 4 presents limitations of current IoT. Although SDN with machine learning algorithms addresses some IoT network issues, significant research issues still need to be addressed through SDN. A high-quality training dataset is required to achieve high accuracy of machine learning models in SDN [221]. There are no standard open-source datasets of IoT network data. Comprehensive IoT network datasets will encourage researchers to analyze IoT network data using machine learning models. IoT network flow data is a challenge to develop heterogeneous IoT applications [222]. SDN-based IoT controller decreases the complexities and obtained flexible IoT management. However, attackers can overload the IoT controller through a massive flow of requests. Machine learning-based approaches such as generative adversarial network (GAN) [206] is a practical approach to solve this vulnerability by predicting the new attacks. There are two neural networks in GAN; one neural network generates new data while evaluating the new data authenticity according to a real dataset. GAN generates possible new attacks based on existing attack data; machine learning models can be trained using the new attack data to detect both known and unknown attacks. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Literature** & **Approach** & **Complementary Technologies** & **Relative Merits** \\ \hline Smart homes [215] & Residential functionalities virtualization & NFV and VNF & Low cost, Security updates with no service disruption, flexible gateway but there are limitations of VNF distribution and Management of tunnels. \\ Centralized IoT devices management [216] & Software development for virtualization & No & Network management for monitoring smart home sensors. \\ Vehicular networks [217] & Software-defined architecture & No & Maintain functional state stability, reduce control overhead, high rates of delivered data but poor resources allocations and management using the controller. \\ Smart grid application [218] & IIoT SDN platform & NFV & Reliable and flexible to adapt to natural troubles or failure. Backup to the smart grid resiliency but fault detection and latency management is poor. \\ E-health medical applications [219] & Control IoT using SDN & No & Transmission optimization during high peak load. \\ Smart healthcare systems [220] & Traffic management using SDN & No & Heterogeneous traffic management, improve reliability and usability but management functionalities. \\ \hline \end{tabular}
\end{table} 
Table 6: _Cont._IoT devices' scalability raises new issues regarding QoS and security, which can be addressed through machine learning-based SDN solutions. CISCO IoX is one such solution for flexible device management in a real-time IoT environment. To ensure QoS and security in the IoT domain, Microsoft proposed architecture to implement data analytics over the network's edge. Many intelligent applications were proposed to collect data, such as IBM Watson, to develop cognitive systems. However, the realization of IoT is still challenging from both a practical and theoretical perspective [223]. Lack of compatibility means that thousands of research were published in the IoT domain proposing IoT platforms and architectures, raising the need for new network schemes. IoT realization involves many parties, network services providers, data services providers, device manufacturers, and application developers. IoT devices manufacturing companies develop monitoring tools to maintain and manage their devices. Some of the manufacturers provide cloud-based solutions for IoT devices and services management.