## 5 Proposed Adaptive Beaconing System
### Characterization of Node Distribution
#### 5.2.3 Defuzzification
In that case, \(NTP\) will be equal to \(CTP\) increased by one step size, since the spaces between clusters are large. In case the quadrat value is near one, the vehicles are uniformly randomly distributed in 2D. In that situation, \(NTP\) is equal to \(CTP\). Otherwise, \(NTP\) is equal to \(CTP\) decreased by one step size, since it means that the nodes are more evenly spaced. Algorithm 2 shows the steps of adapting the transmission power in HACEAB. Figure 5: HACEAB membership function for the input SINR factor. ```
1:if\(ND\) > \(DT\)then
2:\(NTP=CTP-\) step size
3:else
4:if Q > 1 then
5:\(NTP=CTP\) + step size
6:else
7:if Q \(\approx\) 1 then
8:\(NTP=CTP\)
9:else
10:\(NTP=CTP-\) step size
11:endif
12:endif
13:endif
```

**Algorithm 2** Transmission power method. Figure 8: HACEAB membership function for the input number of neighbors factor. Figure 6: HACEAB membership function for the input channel busy time factor. Figure 7: HACEAB membership function for the input packet delivery ratio factor. Figure 9: HACEAB membership function for the input mobility factor. ## 6 Simulation and Results

To evaluate the performance of HACEAB, an NS-3 network simulator was utilized, which is based on C++ [62]. The simulation parameters are shown in Table 6. We carried out a comprehensive evaluation of HACEAB including covering both highway and urban environments to ensure the robustness and adaptability of HACEAB in various scenarios. First, the performance of our protocol was compared against the schemes which were based only on Set 1, Set 2, or Set 3's parameters. Then, the performance of HACEAB was compared with other protocols which were a fixed rate of 10 beacons per second (a requirement of many safety applications [5]), JSRC [19], and BRAIN-F [20]. Finally, it was shown how adaptive transmission power could improve the protocol performance. The performance was evaluated based on three metrics as follows:

* AverageWaiting Time (AWT): it is the duration during which CCH is noted as being occupied due to transmissions from nearby vehicles. * Total Delay: It is the duration it takes for a packet to be delivered from the source to the destination. This metric indicates the freshness of the information. * AverageNumber of Neighbors: It is the average number of discovered neighbors within the transmission range. This metric reflects the cooperative/neighborhood awareness. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of vehicles & 10, 50, 100, 150, 200 \\ \hline Packet size & 400 bytes \\ \hline Data rate & 6 Mbps \\ \hline MAC/PHY protocol & IEEE 802.11p \\ \hline Signal propagation & Nakagami \\ \hline Reception threshold (\(Rx_{Th}\)) & \(-91\) dBm \\ \hline CCA threshold & \(-95\) dBm \\ \hline Simulation time & 300 s \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The simulation parameters. Figure 11: HACEAB membership function for the output final congestion rank. Figure 10: HACEAB membership function for the output preliminary congestion rank. ### Performance of HACEAB

We evaluated the performance (only the beaconing frequency adaptation with no transmission power adaptation) of HACEAB and compared it with protocols that used only Set 1, Set 2, or Set 3's parameters to confirm that it could intelligently combine their strength in nondense and dense conditions in both clustered and uniformly distributed environments. #### 6.1.1 Highway Environment

The vehicles' mobility was created based on a constant speed mobility model in NS-3. The position allocation in our simulation was based on the NS-3 random rectangle position model [63]. In this model, vehicles are distributed uniformly on a straight line (highway road scenario). We conducted simulations utilizing the WAVE model [64]. The WAVE model constitutes the comprehensive system architecture constructed in NS-3 for vehicular communications.