## 3 Results
GPSR:TOM improves congestion handling but may introduce rout  
Figure 11: Throughput and variation comparison between AODV, AODV:TOM, and AODV:DEM in city and highway scenarios for different packet sizes. Figure 10: Throughput and variation comparison between GPSR, GPSR:TOM, and GPSR:DEM in city and highway scenarios. ing inefficiencies in sparse networks, while GPSR:DEM optimizes energy and density management at the cost of increased overhead. FSR reduces routing overhead but suffers from outdated information in dynamic networks, whereas DSR, despite its adaptability, incurs high overhead and latency in large-scale deployments. These limitations highlight the trade-offs in protocol selection based on network mobility, scalability, and real-time performance requirements. Figure 12: Throughput and variation comparison between GPSR, GPSR:TOM, and GPSR:DEM in city and highway scenarios for different packet sizes. Figure 13: Throughput and variation comparison between AODV, AODV:TOM, and AODV:DEM in city and highway scenarios for different mobility speeds. Moreover, from the figures, the throughputs of AODV, AODV:TOM, AODV:DEM, GPSR, GPSR:TOM, and GPSR:DEM can be observed. The throughput of most protocols increases proportionally with pause time, except for GPSR:DEM, which demonstrates a linear throughput trend across all pause times. This linear trend indicates that GPSR:DEM maintains a steady performance regardless of mobility pauses, contrasting with other protocols that show a more dynamic response. For example, at pause times below 100 s, GPSR:DEM achieves 60 Mbps in both city and highway, while GPSR reaches only 54 Mbps. However, as the pause time increases beyond 200 s, GPSR throughput surpasses GPSR:DEM, indicating that DEM profiles are more beneficial for scenarios with frequent mobility pauses. The TOM and DEM profiles effectively improve the throughput of both AODV and GPSR across all pause times compared to their baseline counterparts. AODV:TOM consistently outperforms the baseline counterparts, which is the most important for the proposed approach. Figure 14: Throughput and variation comparison between GPSR, GPSR:TOM, and GPSR:DEM in city and highway scenarios for different mobility speeds. Figure 15: Throughput and variation comparison between AODV, AODV:TOM, and AODV:DEM in city and highway scenarios for different pause times. outperforms AODV, showing an improvement of approximately 10% for pause times between 50 to 150 s in both city and highway scenarios. For instance, at 150 s of pause time, AODV:TOM achieves 62 Mbps in the city compared to 56 Mbps for AODV. Similarly, AODV:DEM shows a throughput improvement of 8-12%, especially in highway scenarios with higher mobility. Notably, AODV:DEM achieves 65 Mbps at 300 s of pause time, compared to 60 Mbps for AODV, indicating the impact of DEM optimization in reducing mobility-related throughput drops. The variation in throughput between city and highway scenarios differs across protocols. AODV:TOM shows an increasing variation proportional to pause time, suggesting that its throughput improvement is more pronounced in highway scenarios with longer pauses. For example, the throughput difference between city and highway scenarios for AODV:TOM increases from 3 Mbps at 50 s to 8 Mbps at 300 s. On the other hand, AODV:DEM shows a decreasing variation proportional to pause time, indicating better throughput stability as pause times increase. At pause times of 300 s, AODV:DEM shows a variation of less than 1 Mbps between city and highway, highlighting its reliability in scenarios with long stationary periods. GPSR:TOM and GPSR:DEM both show minimal variation in throughput between city and highway scenarios, making them suitable for both urban and highway environments. GPSR:TOM maintains a throughput variation below 0.005 Mbps across all pause times, while GPSR:DEM shows a slightly higher average variation of 0.02 Mbps, particularly in scenarios with frequent mobility pauses.