# A New SDN-Based Routing Protocol for Improving Delay in Smart City Environments
## 6 Performance Evaluation  
To assess the performance of the SDN-Naive Bayes-based routing protocol, we developed a simulation testbed, a virtual network created through virtual machines configured with ubuntu 18.10 Operating system, Mininet-WiFi 2.7 a wireless SDN emulator [43], SUMO 1.4.0 mobility simulator and OpenFlow 1.3 SDN protocol. The vehicles move on road network size 1500 \(\times\) 2000 m\({}^{2}\) of Montreal downtown. In such topology, we consider all the nodes connected constitute a mesh network. Moreover, to generate traffic, we install the Iperf and ping applications to carry out the data transmission between vehicles. Table 2 summarizes the remaining emulation parameters of our experimentation; we repeat the evaluation of the performance ten times with a confidence interval of 95%. To analyze the performance of the proposed routing protocol, we compare it to three routing protocols of the literature, multipath routing protocol [44], Q-learning protocol [45], and OLSR protocol [46]. The multipath routing protocol and Q-learning protocol are SDN routing protocols, except that OLSR is a Mobile Ad-Hoc Network (MANET) routing protocol. We decide to consider OLSR as comparative routing, since the main operation of this MANET routing is close to SDN working paradigm. OLSR apply centralized routing management executed on multipoint relays nodes that are the responsible nodes in control packet transmission. The following performance metrics measure the efficiency of the evaluated protocols [47]:  
* PDR (Packet Delivery Ratio): the rate of data packets successfully delivered to data packets sent;
* Throughput describes an amount of data transfer by time unit. The compute is the ratio of packets size to the time difference between its sent and its reception;
* Packet Jitter Derivation (Jitter): is a variation in delay between packets. It defines the consistency and stability of the proposed wireless network. Components in the communication path introduce jitter in the system;
* Loss Ratio is the number of packets lost divided by the packets sent during simulation time;
* End to end delay: average time is taken by a data packet to reach the destination. Figure 7 illustrates the packet delivery ratio transmitted by vehicles to a destination. The overall appearance of curves (a) or (b) shows a decrement in the delivery rate. Therefore, the more the number of vehicles in the network increases, the more the delivery rate decreases. This situation is the result of using a single controller to handle all network requests, leading to controller overload. Overall, the Naive Bayes-based protocol shows better results than OLSR, multipath and Q-learning protocols. The following figures confirm the degradation of communication in the presence of a dense network. However, Figure 8 shows a loss ratio of more than 50% of the packets sent when the number of nodes exceeds 50 vehicles. Nevertheless, beware of our solution's objective, which brings an \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Network size & 10–130 vehicles, 10 RSU and one controller \\ \hline Simulation area & 1500 \(\times\) 2000 m\({}^{2}\) \\ \hline Propagation loss & Two-Rays Ground \\ \hline Mobility & SUMO real scenario \\ \hline Simulation time & 500 s \\ \hline Packet size & 512 bytes \\ \hline transmission range & 250 m, 500 m \\ \hline Transport protocol & UDP \\ \hline Mac protocol & 802.11p \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Emulation Parameters. improvement compared to other comparative solutions. Our protocol performs better than others with less packet loss, because it uses prediction based on the area where the destination is located, which provides enough coverage to ensure a routing path between the source-destination pair. On the other hand, the Multipath, Q-learning, and Naive Bayes protocol's throughput are very similar and better than OLSR.