## 3 Methodology
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**S.No.** & **Existing Work** & **Summary** & **Limitations** \\ \hline \multirow{4}{*}{3} & \multirow{4}{*}{[13]} & \multirow{4}{*}{Impact of heterogeneity on QoS is showed by author. The service time of the controller and flow arrival time relationship with heterogeneity is presented. A mechanism is developed to reduce the response time of the controller and alleviate heterogeneity by making a cluster of controllers so it can be treated as homogeneous.} & \multirow{4}{*}{Tested in very ideal conditions, as bandwidth controller and network delay parameters are not considered. Other algorithms of schedule are applied to see any improvement. Combining security methods with this framework is not tested.} \\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{[3]} & \multirow{4}{*}{To maintain QoS in the SDN-IoT network, the best path is selected between the source and server. This method makes decisions on server capacity, available bandwidth, and the classification of traffic. Fuzzy logic is implemented on the SDN controller.} & \multirow{4}{*}{This method only focuses on load balance on the basis of server capacity. It is only applicable for centralized single controllers, not for distributed ones.} \\ \hline \multirow{4}{*}{5} & \multirow{4}{*}{[43]} & \multirow{4}{*}{To overcome heterogeneity in SDN-IoT, a centralized SDN controller can self-adapt by self-observing the user request. This approach is applied by the author to improve QoS. It uses heuristic routing based on the Lagrange relaxation theory and ontology created for analyzing user requests.} & \multirow{4}{*}{This system adapts according to the user’s current needs. The approach is only tested in a simulation; the real-world implementation has not yet been performed. Parameters like controller response time and capacity are not considered.} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._basis of these service rates, the central controller divides these distributed heterogeneous controllers into homogeneous groups. In this approach, the controller belonging to the same group is treated as one homogeneous controller. Any mathematical module or theory on homogeneous networks can be implemented here. We implement the pull-based scheduling approach to collect statistics from the peer controllers. To fetch the statistics of the service rate from the peer controller, the NBI parser is used in the central controller. These collected statistics are then forwarded into a sub-module known as the state-collection module and database. From the collected data in the state-collection module, the peer controllers are divided into groups. Grouping is performed by group formation and queuing modules. After forming the groups, the group formation and queuing module fetch the QoS requirement from the incoming flow. This offloads the flow request towards a group that satisfies the application or flow-specific QoS requirements. The proposed framework architecture [13] is presented in Figure 4.  
This methodology improves QoS in IoT networks by reducing controller response time and increasing real-time attack detection while maintaining security. The approach divides controllers into homogeneous groups based on service rate, transforming heterogeneous controllers into homogeneous ones. This experimental study builds on [13] by introducing real-world parameters, such as bandwidth, using a network from the Internet Zoo Topology. Various simulations were conducted under different conditions, including changes in the number of hosts and protocols, with results compared to the base paper.