## 4 Performance Evaluation
### Simulation Setup  
The performance evaluation of our proposed resilient routing protocol was conducted using the NS-3 simulation environment. We conducted the simulation 50 times with a 95% confidence interval. The simulation scenario consists of a fixed number of 100 nodes deployed within a network area of 4000 m by 4000 m. Nodes are deployed using a random waypoint mobility model with speeds ranging from 5 km/h to 25 km/h to simulate the high mobility typical of MANETs. The simulation duration is set to 3600 s, with a pause time of 0 s to maintain continuous movement. Detailed simulation parameters are provided in Table 1.  
Two distinct experimental setups are employed to evaluate different aspects of the protocol:* Speed Variation: Traffic bit rate is fixed at 3000 bps, and node speeds vary from 5 km/h to 25 km/h.
* Traffic Bit Rate Variation: Node speed is fixed at 15 km/h, and traffic bit rate varies from 1000 bps to 5000 bps.  
Traffic is generated using a constant bit rate model with a packet size of 512 bytes and varying transmission rates as specified above. Experiments were conducted to validate that a clustering interval of 300 s ensures optimal performance. Intervals of 100 and 200 s incur excessive overhead due to frequent re-clustering, while intervals of 400 and 500 s result in poor path stability because they cannot quickly adapt to topology changes. In contrast, intervals of 300 s provide the best trade-off by achieving higher PDR and lower end-to-end delay without incurring unnecessary overhead. The experimental results are shown in Figure 2.  
The effectiveness of our protocol is evaluated using several key performance metrics. PDR measures the ratio of packets successfully delivered to the destination against those sent by the source, indicating the reliability of the protocol. End-to-End Delay assesses the time taken for a packet to travel from the source to the destination, reflecting the efficiency of the routing path. Average Energy Consumption evaluates the average energy consumed by all nodes during the simulation, highlighting the protocol's energy efficiency. These metrics provide a comprehensive understanding of the protocol's performance under varying traffic conditions.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Simulation area & 4000 m \(\times\) 4000 m \\ Number of nodes & 100 \\ Node mobility model & Random Waypoint \\ Node speed & 5 km/h to 25 km/h \\ Simulation Duration & 3600 s \\ Transmission range & 250 m \\ Traffic type & Constant bit rate \\ Packet size & 512 bytes \\ Transmission Rate & 1000 to 5000 bits/s \\ Clustering Update Interval & 100 s to 500 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.  
Figure 2: Performance by clustering update interval variation.  
To evaluate the performance of our proposed resilient routing protocol, we compare it against two existing machine learning-based protocols: Q-Learning-based Routing Protocol (QLR) and Deep Q-Network-based Routing Protocol (DQR). QLR utilizes Q-learning to dynamically select optimal routes based on environmental feedback, enhancing adaptability and performance in changing network conditions. According to [4], Q-Routing has already demonstrated superior performance compared to AODV. Therefore, instead of re-evaluating AODV in this work, we concentrate on learning-based protocols that more closely align with our proposed approach, ensuring a direct and relevant comparison. DQR leverages Deep Q-Networks to predict and optimize routing paths, offering improved decision-making capabilities through advanced pattern recognition and prediction. By comparing our protocol with these machine learning-enhanced protocols, we highlight the advantages of integrating DRL and deep learning techniques within our proposed SDN framework.