## 5 Proposed Adaptive Beaconing System
### Characterization of Node Distribution
#### 5.2.3 Defuzzification
Figure 12 shows that HACEAB maintains a low average waiting time compared to Set 2 and Set 3's protocols for 10, 50, 100, and 150 vehicles (not dense), and less than Set 1 and Set 2's protocols for 200 vehicles (dense). This is because HACEAB uses the parameters of Set 1 in nondense clustered environment, while it uses the parameters of Set 3 in a dense highway environment (Table 1). Similarly, from Figure 13, we can notice that HACEAB has a smaller delay than Set 2 and Set 3's protocols for 10, 50, 100, and 150 vehicles, while it has a smaller delay than Set 1 and Set 2's protocols for 200 vehicles. The main aim of an adaptive beaconing scheme is to enhance cooperative awareness and maintain information freshness without inducing network congestion. Figure 14 illustrates that HACEAB achieves a closer level of awareness compared to Set 1 and Set 2's protocols for 10, 50, 100, and 150 vehicles, and is closer to Set 2 and Set 3's protocols for 200 vehicles. Figure 12: Average waiting time in a highway environment. #### 6.1.2 Urban Environment

In order to obtain results for an urban environment, we used the network simulator, NS-3. We employed a Manhattan grid configuration of dimensions \(3\times 3\), featuring an edge length of 1000 m, with a spacing of 500 m between any two intersections. To obtain a realistic simulation, Simulation of Urban MObility (SUMO) [65] was used to generate the vehicles'

Figure 14: Average number of neighbors in a highway environment. Figure 13: Total delay in a highway environment. movement. At the onset of each simulation, vehicles were dispersed randomly across the road network. As the simulation advanced, vehicles started moving in accordance with the car-following model, where a vehicle's speed is influenced by the speed of the vehicle ahead. Random routes were generated using the randomTrips utility within the SUMO framework. Subsequently, the resulting mobility traces were imported into NS-3 to simulate node mobility. Figure 15 shows that HACEAB has a smaller average waiting time than Set 2 and Set 3's protocols for 10 and 50 vehicles, and a smaller one than Set 1 and Set 3's protocols for 100 and 150 vehicles. For 200 vehicles, HACEAB has less waiting time than Set 1 and Set 2's protocols. From Figure 16, it can be observed that HACEAB performs closer to Set 1 and Set 2's protocols for 50 vehicles and better than Set 1 and Set 3's protocols for 100 and 150 vehicles (nondense urban) in terms of delay. For 200 vehicles (dense urban), HACEAB performs better than Set 1 and Set 2's protocols, since it uses the parameters of Set 3 (Table 1) as the local density (\(ND\)) is larger than the density threshold (\(DT\)). Similarly, in Figure 17, we can see that HACEAB detects a higher number of neighbors than Set 3's protocol for 10, 50, 100, and 150 vehicles, and a higher number than Set 1's protocol for 200 vehicles. ### Performance of HACEAB Compared To Other Protocols

In the previous section, the performance of HACEAB was shown to combine the strength of the protocols that used only Set 1, Set 2, or Set 3 in both dense and nondense highway and urban environments. Here, HACEAB, functioning as an adaptive beaconing rate protocol, is compared with a fixed rate of 10 beacons per second. Additionally, it is compared with two other adaptive beaconing rate protocols, namely JSRC and BRAIN-F, within both highway and urban environments. #### 6.2.1 Highway Environment

From Figure 18, we can clearly see that the average waiting time of the fixed rate of 10 beacons per second is the highest. This means fixed-rate beaconing is inefficient in terms of channel usage, since the beaconing rate is always the same regardless of the channel status. JSRC behaves closer to the fixed-rate protocol for low density and improves as density increases, since it reduces the beaconing rate as the number of vehicles increases. As for BRAIN-F, it uses the channel more efficiently than the fixed rate and JSRC for 10, 50, 100, and 150 vehicles. However, for 200 vehicles, JSRC performs better.