# A New SDN-Based Routing Protocol for Improving Delay in Smart City Environments
## 4 Network Model  
This section describes the proposed network model used to design the SDN-Naive Bayes-based routing protocol in a smart city environment. Mainly, our work focuses on intelligent transportation systems that represent the lifeblood of smart cities. The network and their operation components take advantage of the SDN paradigm and ML to offer efficient communication and aid to bring more  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Dataset Size**} & **50\% Train Stage** & **60\% Train Stage** & **70\% Train Stage** & **80\% Train Stage** \\  & **50\% Validation Stage** & **40\% Validation Stage** & **30\% Validation Stage** & **20\% Validation Stage** \\ \hline
10,000 & 0.9164 & 0.9102 & 0.9056 & 0.901 \\ \hline
50,000 & 0.9077 & 0.9083 & 0.9092 & 0.9078 \\ \hline
150,000 & 0.9142 & 0.9147 & 0.9164 & 0.9179 \\ \hline
250,000 & 0.9162 & 0.9171 & 0.9181 & 0.9170 \\ \hline
500,000 & 0.9147 & 0.9137 & 0.9136 & 0.9128 \\ \hline
1,000,000 & 0.9298 & 0.9296 & 0.9301 & 0.9303 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy evaluation and splitting dataset ratio selection.  
Figure 4: Matrix correlation reporting Pearson’s correlation coefficients of each pairs of Features Dataset.  
flexibility and agile to the system. Our network is composed of three types of equipment. The following components are required for deploying the system.  
* Controller. It plays a central role in the network as the heart and concretize the control plane. The controller has the global intelligence provided by the overview of the whole system and knowledge of all devices in the network. This equipment controls all the network devices. In this paper, we choose the Ryu controller for its suitability to integrate a large selection of applications. Ryu controller development matches very well with ML programming API, since they use the same language, i.e., Python.
* Roadside units (RSU) considered virtual switches. This material is the relay between the controller and the host nodes. They transmit all the rules and modifications in the network.
* Vehicles. They are the fundamental part of the network and represent SDN Wireless Nodes. The cars communicate actively with their neighbourhood and act as the end-users and forwarding element. They are the first source of data in smart transportation systems. The big challenge with these devices is their high mobility, frequent arrival and departure in the network.  
The RSU and vehicles constitute the data plane. They operate with the OpenFlow protocol to communicate with the controller. The proposed network model works in a heterogeneous network environment in which the forwarding infrastructure uses wireless and wired technologies to deliver information; the WiFi 802.11P to connect a vehicle to RSU infrastructure (V2I), and the optical technology to connect the RSU to the controller. Figure 5 highlights the network components and used communication technologies. As displayed, the communication V2V and V2I employ wireless connection. Each vehicle is equipped with the WiFi 802.11P interface to exchange messages with RSU in mesh connection. However, the RSU is equipped with two interfaces, one dedicated to wireless communication to transmit information to vehicles. The second interface used to communicate with the controller to forward the rules to the hosts. In the next section, we describe the suggested routing algorithm assisted by the supervised Naive Bayes classifier to reduce delay transmission and network overhead.