## 3 Results
At 100 s of pause time, GPSR:TOM achieves 64 Mbps, compared to 58 Mbps for GPSR in the city and 66 Mbps on the highway. This minimal variation suggests that TOM profiles are more effective in maintaining throughput consistency, regardless of the environment. The observed trends are consistent with findings in the literature. According to [11], pause time significantly impacts the throughput performance of routing protocols in vehicular networks, with optimized profiles like TOM and DEM improving throughput stability. Likewise, study [28] reported that TOM profiles effectively handle scenarios with varying pause times by reducing the impact of frequent mobility changes on throughput, which aligns with the performance of AODV:TOM and GPSR:TOM in this study. On the other hand, reference [34] highlighted that DEM profiles improve throughput during high-mobility scenarios but may exhibit less improvement during extended stationary periods, consistent with the behavior of GPSR:DEM for longer pause times. In summary, TOM and DEM profiles significantly enhance throughput performance across different pause times, with AODV:TOM and GPSR:TOM showing better stability and lower variation between city and highway scenarios. GPSR:DEM provides a steady throughput across all pause times but is outperformed by GPSR for longer pause times [35]. These findings emphasize the importance of incorporating TOM profiles to improve throughput in real-world vehicular networks with varying mobility patterns and pause times, which could be employed in Smart Cities. A study [22] for urban road scenarios compared optimizations using link state routing (OLSR) and dynamic source routing (DSR) protocols in VANETs, simulated using NS-2. The analysis, based on parameters like packet delivery ratio, delay, overhead, throughput, packet loss, and collision rate, shows DSR outperforming OLSR with higher packet delivery and lower packet loss. DSR's superior performance includes a 99.92% packet delivery ratio and 0.0894 ms end-to-end delay, while OLSR has slightly lower results, showing similar trends with the current study. Figure 17 presents a comparative evaluation of the throughput performances of different routing protocols, including AODV, GPSR, CM-AODV, and CM-GPSR, under varying packet sizes. The analysis focuses on two categories: (a) AODV, GPSR, and CM-AODV and (b) CM-GPSR. Throughput, defined as the rate of successfully transmitted data packets over a network channel (measured in Mbps), is examined concerning different packet sizes. The observed results indicate that throughput is directly proportional to the packet size, as larger packets reduce protocol overhead and improve transmission efficiency. At a packet size of 1000 bytes, AODV:TOM and AODV:DEM throughput increased by 29% (0.35 to 0.45 Mbps), GPSR:TOM improved by 100% (0.20 to 0.40 Mbps), while GPSR:DEM showed a marginal increase of 0.05 Mbps. The smaller improvement in GPSR:DEM suggests that the optimization mechanism in CM-GPSR might not be as effective in all scenarios, likely due to GPSR's reliance on geographical routing, where link stability is affected by node density and mobility. These results highlight the impact of optimization on routing efficiency, with CM-AODV and CM-GPSR demonstrating better route selection, mobility handling, and adaptive parameter tuning that enhance throughput. The findings further emphasize that throughput is influenced by routing parameters such as mobility models, transmission power, and congestion control mechanisms. AODV, as a reactive protocol, benefits from reduced control overhead, whereas GPSR, a geographical routing protocol, performs better in dense networks but may suffer in highly mobile environments. The optimized versions, CM-AODV and CM-GPSR, enhance routing efficiency through congestion-aware mechanisms, dynamic metric adjustments, and improved path selection strategies.