## 3 Results and Discussion
### Average Delay Comparison  
Figure 7 shows the average delay of QOS-AODV, GPSR, CM-QOS-AODV, and CM-GPSR across varying traffic generation rates, providing insights into each protocol's ability to handle network delays under different traffic loads. Among all protocols, GPSR achieved the lowest delay, averaging 0.01 s, highlighting its efficiency in maintaining low latency even as traffic increases to the worst-case scenario up to 95%. QOS-AODV also performed well, with an average delay below 0.025 s. These results suggest that the TOM profile in both protocols is effective in optimizing not only throughput and PDR but also minimizing delay, making it suitable for time-sensitive applications within Vehicular Communication Networks (VCNs), Figure 7a. A notable observation is the contrasting behavior of the protocols in relation to traffic generation. For GPSR, delay increased proportionally with higher traffic generation, indicating that these profiles become less efficient as traffic loads rise. This is likely due to congestion in the routing process, where higher traffic leads to longer packet queuing times and slower delivery. On the other hand, QOS-AODV displayed an inverse relationship between traffic generation and delay, suggesting that as traffic increased, QOS-AODV became more efficient in handling packets, possibly due to its reactive nature, which allows it to adapt dynamically to changing network conditions. QOS-AODV, while still improving over the baseline QOS-AODV, exhibited performance that was more stable across different traffic loads but could not achieve the low delays observed in TOM profiles. The delay consistency observed in GPSR, QOS-AODV, and QOS-AODV is an important indicator of their robustness to fluctuations in traffic generation, as shown in Figure 7b. Despite varying levels of traffic, these profiles managed to keep the delay within a narrow range of around 0.01 s, indicating that they are well-optimized for maintaining low and stable latency under different traffic conditions up to 95% worst case. This robustness is critical in VANET environments, where traffic loads can vary dramatically due to the movement of vehicles and the changing density of network nodes. The ability to maintain low delays ensures more timely communication, which is essential for applications requiring rapid data exchange, such as collision avoidance systems or real-time traffic management. In conclusion, the analysis of average delay across different protocols and traffic generation rates highlights the superior performance of TOM profiles, particularly GPSR, in minimizing delay. The consistently low delay observed in both TOM and QOS-AODV profiles demonstrates their adaptability and robustness in handling various traffic loads. However, the proportional increase in delay observed in GPSR and the baseline GPSR points to the challenges these protocols face in high-traffic environments. This suggests that while TOM profiles are well-suited for delay-sensitive applications in VCNs, further optimization may be needed for GPSR to improve its performance under heavier traffic loads. Comparison Between the Average Throughputs for PDR and Delay Obtained Against Traffic Generations and Packet Sizes  
The throughput comparison in Figure 8 shows that QOS-AODV-TOM and GPSR-TOM achieve the highest throughput under all traffic generation intervals, outperforming their respective baseline protocols (QOS-AODV and GPSR). QOS-AODV-TOM consistently performs above 0.40 Mbps, while GPSR-TOM surpasses 0.47 Mbps at higher traffic loads. This demonstrates the efficiency of TOM profiles in optimizing network parameters for better data transmission performance. TOM's optimization focus on throughput helpsmaintain a steady flow of data even as traffic increases, minimizing packet loss and network congestion. QOS-AODV-DEM and GPSR-DEM, though improved compared to their baseline versions, still show a slight decrease in throughput compared to TOM profiles.