## 3 Results and Discussion
### Average Delay Comparison
This is likely due to DEM's consideration of multiple factors (PDR, delay, and throughput), which may slightly compromise the throughput to improve overall performance balance. In comparison with the recent literature, throughput optimization techniques like TOM show considerable advancements in routing protocol efficiency for Vehicular Communication Networks (VCNs). Studies, such as those by [PERSON]. [21], emphasize the importance of throughput in VCNs due to the high mobility and dynamic topology of nodes. Protocols that emphasize throughput, like TOM, are highly desirable for real-time applications like collision avoidance and traffic management. The improvements seen with TOM profiles align with the trends observed in hybrid protocols like those discussed by [PERSON]. [22], where hybrid routing techniques were employed to maintain high data rates in challenging VANET environments. The results here reinforce that optimized profiles significantly enhance throughput without compromising other performance metrics. Figure 8: Comparison of average throughputs across varying traffic generations, packet sizes. (**a**) Throughput vs. traffic generation; (**b**) throughput vs. packet size. Figure 7: Delay comparison between (**a**) QOS-AODV, QOS-AODV-TOM, and QOS-AODV-DEN and (**b**) GPSR, GPSR-TOM, and GPSR-DEN for different traffic generations. As mentioned above, Figure 9 presents a comprehensive comparison of the average throughputs observed in three different protocols: CM-QOS-AODV, CM-QOS-AODV, and IOLSR in both city and highway scenarios. Throughput is a key performance metric that indicates how effectively a network protocol delivers data over a communication channel by comparing the performance of these protocols in various conditions, such as traffic generation and packet size. In terms of traffic generation, all three protocols, CM-QOS-AODV, CM-QOS-AODV, and IOLSR, show relatively consistent performance, with their throughputs ranging between 0.06 and 0.08 Mbps. This narrow range suggests that the protocols exhibit similar efficiency in handling traffic under varying load conditions, regardless of the city or highway environment. Such close throughput values imply that none of the protocols have a distinct advantage over the others when subjected to moderate traffic conditions. However, the observation that the throughput values for all protocols remain within a relatively narrow range may be attributed to the overall low traffic conditions or the balanced distribution of network load across protocols during the evaluation. To further assess the scalability and robustness of these protocols, future work should incorporate high-traffic scenarios or targeted stress tests to evaluate performance under more demanding network conditions. When analyzing the impact of varying packet sizes, the protocols generally exhibit throughput values ranging between 0.15 and 0.25 Mbps, with the notable exception of IOLSR in the highway scenario, where throughput significantly declines. This decline underscores the limitations of proactive routing protocols like IOLSR in dynamic and rapidly changing environments, such as highways [21]. Proactive protocols continuously update routing tables to maintain a complete view of the network topology, which introduces substantial overhead. In high-mobility scenarios, this overhead becomes increasingly burdensome due to frequent topology changes caused by fast-moving vehicles, resulting in degraded throughput performance. In contrast, reactive protocols such as CM-QOS-AODV demonstrate greater adaptability in such environments. Rather than maintaining constant routing information, these protocols discover routes only when needed, minimizing unnecessary overhead and better coping with dynamic topology shifts [22]. This on-demand nature makes reactive protocols particularly suitable for high-mobility vehicular scenarios, where rapid adaptability and reduced control message overhead are essential for maintaining reliable communication.