## 5 Proposed Adaptive Beaconing System
### Characterization of Node Distribution
#### 5.2.3 Defuzzification
#### 6.3.2 Urban Environment

Figure 27 illustrates the efficient use of the channel by HACEAB when the transmission power adaptation is on compared to when it is off, since HACEAB determines the node distribution and the network density status before it adjusts the transmission power. In Figure 28, the packet delay when the transmission power adaptation is on is almost the same as when it is off for 10 and 50 vehicles and better for 100 and 150 vehicles. Yet, for 200 vehicles, when the transmission power adaptation is off, the protocol maintains a smaller delay than when it is on, since more nodes are covered when HACEAB increases the transmission power. Figure 29 shows that HACEAB achieves a higher level of cooperative awareness when transmission power adaptation is on than when it is off. Figure 11:3 Average number of neighbors in a highway environment. Figure 11:4 Total delay in a highway environment. Figure 11:5 Total delay in an urban environment. Figure 11:6 Average waiting time in an urban environment. Figure 11:7 Average number of neighbors in an urban environment. ## 7 Conclusions

A beacon is a periodic message that is sent to all one-hop neighbors and contains basic information about the sending vehicle, which is needed to support safety and nonsafety applications. The rate at which this information is updated has to be high enough to maintain its freshness without unnecessarily congesting the network. This motivates the need for adaptive beaconing to cope with mobility and network density variations in VANETs. Our preliminary work led us to establish, through extensive simulation experiments, that specific input parameters played a more crucial role in adapting beacon rates to certain environments and conditions. Building upon that insight, we introduced HACEAB, an innovative adaptive beaconing scheme based on fuzzy logic, designed for heightened awareness and channel efficiency. This scheme was tailored to adapt to various environments and conditions. Initially, HACEAB estimates network density using an adaptive threshold function to discern the prevailing network condition, distinguishing it as either dense or not. Afterwards, the protocol assesses the spatial distribution of nodes through the quadrat statistical method to classify the network environment as either clustered or uniformly distributed. Subsequently, it adjusts the beaconing rate by utilizing the relevant fuzzy logic system input parameters based on the determined network condition and environment. Additionally, it enhances performance by adapting the transmission power to accommodate changes in node density and distribution. Our simulation results confirmed the ability of HACEAB to intelligently adapt to different densities and spatial distribution of nodes. We compared HACEAB's performance to that of fixed-rate beaconing, JSRC, and BRAIN-F. HACEAB achieved remarkable improvements compared to the fixed-rate beaconing in terms of neighborhood awareness, channel usage, and packet delay for both highway and urban environments. Moreover, HACEAB outperformed BRAIN-F across all density levels, while substantially outperforming JSRC for lower density levels in terms of channel usage and packet delay. As for neighborhood awareness, HACEAB detected a higher number of neighbors than JSRC and BRAIN-F across all density levels in both highway and urban environments. The results also showed that when transmission power adaptation was used, further improvement in neighborhood awareness and channel usage was achieved in both urban and highway environments. In the future, and as many applications in vehicular networks depend heavily on beaconing, we plan to investigate the impact adaptive beaconing may have on some of these applications. Conceptualization, M.A. and I.M. ; methodology, M.A. and I.M. ; software, M.A. ; validation, M.A. and I.M. ; formal analysis, M.A. and I.M. ; investigation, M.A., I.M. and E.L.; writing--original draft preparation, M.A., review and editing, I.M. and E.L.; visualization, M.A. and E.L.; supervision, I.M.