## 4 Results
### Routing Performance Evaluation
#### 4.4.4 Impact of Vehicular Mobility  
In this experiment, there were 72 vehicles and 24 RSUs in the vehicular network, and the data collection (\(f\)), nominal optimization (\(f^{\prime}\)), and routing frequencies (\(f^{\prime\prime}\)) were all set to 0.5 Hz. According to Section 4.4.2, the proposed method produced moderate cost, channel utilization, latency, and PDR. The experiment's dependent variable was the maximum speed at which cars were permitted to travel in each of the three mobility scenarios--autobahn, urban, and rural. The findings are shown in Figure 19. The suggested routing technique, Dijkstra, and AODV were assessed for each of the mobility situations by adjusting the maximum permitted speed of the vehicles. When observing the cost variation with mobility, it is evident from Figure 19a that, under any mobility scenario or under any allowable maximum speed, the increasing order of average communication cost is always in the order of proposed routing, Dijkstra, and AODV. Thus, under any mobility scenario, the proposed routing yields the lowest average communication cost. However, the communication cost gap between the proposed routing and other methods is much lower for the rural mobility scenario. For the autobahn mobility scenario, an increasing trend in communication cost for all routing techniques can be observed for speeds in the range 0-50 km/h, and then a clear decreasing trend can be observed for high speeds in the range 50-250 km/h. Similarly, for the rural mobility scenario, an increasing trend in communication cost can be observed for speeds in the range 0-40 km/h, and then a decreasing trend can be observed for speeds in the range 40-100 km/h for all the routing methods. However, for the urban mobility scenario, the communication cost variation among routing methods differs. For the proposed routing in the urban mobility scenario, the cost increases from 0 to 10 km/h and then decreases slowly afterwards, while the cost of Dijkstra increases slowly with speed, whereas for AODV, a clear variation with speed cannot be observed. Furthermore, the cost for each of the methods under any speed in increasing order is rural, urban, and autobahn. The reason for the preceding order is the average network link entropy resulting from node density and the maximum transmission distance in each of the mobility scenarios. For example,  
Figure 19: Routing performance evaluation under various mobility scenarios. (**a**) Communication cost fluctuation for diverse mobility scenarios. (**b**) Channel utilization fluctuation for diverse mobility scenarios. (**c**) Latency fluctuation for diverse mobility scenarios. (**d**) Packet delivery ratio fluctuation for various mobility scenarios. the rural mobility scenario has the lowest node density (3000 m \(\times\) 3000 m/96 nodes) and a moderate maximum transmission distance (41 dBm), resulting in the least average network link entropy and the least communication cost among mobility scenarios. When considering the variation in channel utilization with mobility, the LTE and Ethernet channel utilization of AODV does not vary with mobility scenario or speed and always remains at zero, as evident from Figure 19b. The DSRC utilizations of the proposed method and Dijkstra almost do not vary with speed for a given mobility scenario. However, the increasing order of DSRC utilization for the proposed method and Dijkstra is rural, urban, and autobahn. The preceding order explains the reason for obtaining communication cost variation, also in the exact same order for those two routing approaches. However, the DSRC utilization of AODV tends to vary with speed in a given mobility scenario. Specifically, for the urban mobility scenario, the DSRC utilization of AODV tends to increase slightly with speed; for the rural mobility scenario, it tends to increase with speed except in the range 80-100 km/h; and for the autobahn mobility scenario, a clear variation cannot be observed.