# A Robust Routing Protocol in Cognitive Unmanned Aerial Vehicular Networks
## 6 Simulation Results
### Performance Comparison  
This section offers a comparative analysis of simulation results, evaluating the performance of the proposed protocol against four pre-existing alternatives: MDRMA [25], MA-DP-AoDV [26], MDA-AoDV [26], and SecRIP [17]. According to Figure 9, a common trend is observed across all five routing protocols: end-to-end latency increases as the number of nodes in the network grows. This phenomenon can be explained by the fact that denser networks involve a greater number of nodes participating in the data transmission process. Consequently, this leads to an increase in queuing delays as packets need to be processed by more intermediate nodes before reaching their final destination. Additionally, the likelihood of packet collisions and retransmissions also rises with network density, further contributing to the increased latency. However, when comparing the performance of the proposed routing protocol with the other four protocols, a significant improvement in end-to-end latency is evident. The proposed routing protocol indicates lower latency, which can be attributed to its innovative approach to route selection. Unlike the other protocols that may focus simply on metrics such as hop count or link quality, the proposed protocol integrates the consideration of queuing delays along with other parameters, such as link stability and residual energy, when selecting the optimal routes. This comprehensive evaluation ensures that the selected paths are not only reliable and energy-efficient but also optimized for minimal delay. Figure 8: Comparison of normalized routing loads for packet sizes of 512 and 1024. By proactively managing queuing delays and avoiding congested routes, the proposed protocol effectively reduces the overall time taken for data packets to travel from the source to the destination. This results in a more efficient and responsive network, particularly in scenarios with high node density. The improved performance of the proposed protocol highlights its robustness and adaptability in managing the complexities associated with dynamic and dense network environments, thereby ensuring more consistent and lower end-to-end latency compared to traditional routing protocols. On the other hand, Figure 10 demonstrates that an increase in traffic density leads to a corresponding rise in throughput across all the evaluated routing protocols. Throughput, in this context, refers to the total amount of data successfully delivered to the destination node per unit time. As traffic density increases, more data packets are transmitted within the network, thereby boosting the overall throughput. Among the protocols examined, the proposed routing protocol exhibits superior performance in terms of network throughput when compared to MDRMA [25], MA-DP-AoDV [26], MDA-AoDV [26], and SecRIP [17]. This enhanced throughput performance can be attributed to several key features of the proposed protocol. Firstly, it selects stable routes that are characterized by higher link stability, even in scenarios where spectrum availability is variable. By focusing on link stability, the proposed protocol ensures that data packets are transmitted over reliable paths, thereby reducing the likelihood of link failures and packet loss. Figure 10: Number of nodes vs. Throughput. Figure 9: Number of Nodes vs. Delay. Moreover, the proposed protocol requires fewer control messages to maintain these stable routes. This efficiency in control message overhead further contributes to higher throughput, as more bandwidth is available for actual data transmission rather than being consumed by control signaling. In contrast, the other evaluated protocols demonstrate less stability in environments with variable spectrum conditions. These protocols are more prone to frequent link failures and subsequent packet losses, which negatively impact their overall throughput. The superior performance of the proposed routing protocol in terms of throughput underscores its robustness and adaptability in managing dynamic network conditions.