## 3 Results
For AODV:DEM and GPSR:DEM, the throughput difference between city and highway increases proportionally with packet size. For example, at 500 bytes, the difference is around 6 Mbps for AODV:DEM and 7 Mbps for GPSR:DEM, but at 1500 bytes, the difference increases to 10 Mbps and 12 Mbps, respectively. On the other hand, AODV:TOM and GPSR:TOM show an inverse proportional trend, where the throughput difference between city and highway decreases as packet size increases. For AODV:TOM, the throughput variation reduces from 5 Mbps at 500 bytes to 3 Mbps at 1500 bytes, indicating that TOM profiles provide better stability in varying environments with larger packets. Comparing DEM and TOM profiles across the two protocols reveals differences in how throughput improves across city and highway scenarios. AODV:DEM and AODV:TOM exhibit linear throughput improvements in highway scenarios, with throughput increasing steadily with packet size. In city scenarios, AODV:DEM shows a proportional increase in throughput with packet size, reaching 58 Mbps at 1500 bytes. In contrast, GPSR:DEM demonstrates a linear improvement in city scenarios, achieving 63 Mbps at 1500 bytes, but its throughput improvement in the highway is inversely proportional to packet size, with a smaller gain at larger packet sizes. Notably, GPSR:TOM shows linear throughput improvements in both city and highway scenarios, indicating its robustness in handling varying packet sizes efficiently. For instance, GPSR:TOM records 60 Mbps at 500 bytes and 65 Mbps at 1500 bytes, with a consistent 3-4 Mbps difference between city and highway across all packet sizes. The findings in this study align with reports in the literature. According to [29], larger packet sizes generally improve throughput due to reduced overhead in data transmission, especially in dynamic vehicular networks. However, the study by [30] highlights that increasing packet sizes can also lead to higher packet loss rates in unstable environments, which may explain the throughput variation between city and highway scenarios observed in this study. The performance improvement of the DEM and TOM profiles is consistent with the results in [3], which report that optimization techniques can improve throughput by up to 20% in VANETs. Additionally, reference [4] notes that geographic routing protocols like GPSR benefit significantly from optimization modules in dense traffic environments, which explains the higher throughput of GPSR:TOM and GPSR:DEM compared to baseline GPSR. In conclusion, AODV:DEM and GPSR:DEM achieve the highest throughput across different packet sizes, but their performance shows greater variation between city and highway environments as packet size increases. In contrast, AODV:TOM and GPSR:TOM show more consistent performance across environments, with GPSR:TOM demonstrating linear improvements in both scenarios, making it a robust protocol for real-world vehicular applications. These findings reinforce the importance of integrating optimization modules like TOM and DEM to improve throughput performance in VANETs, particularly when handling varying packet sizes. Figures 13 and 14 illustrate the throughput performance of AODV, AODV:TOM, AODV:DEM, GPSR, GPSR:TOM, and GPSR:DEM across varying vehicle mobility speeds in both city and highway scenarios. The throughput of all protocols shows a linear response to increasing vehicle speeds within a specific range. Notably, TOM profiles of both AODV and GPSR consistently achieve higher throughput than DEM profiles in both environments, except for AODV in the city scenario, where AODV:DEM slightly outperforms AODV:TOM. For instance, at 80 km/h, the observed throughput for AODV:DEM is around 52 Mbps in the city compared to 50 Mbps for AODV: TOM, while in the highway scenario, AODV:TOM reaches 60 Mbps at the same speed, surpassing AODV: DEM's 58 Mbps. The results suggest that TOM profiles provide better adaptability to mobility changes, particularly in dynamic highway scenarios.