## 5 Communication in Vehicular Networks
### Routing  
Vehicular networks allow for the rapid movement of vehicle nodes, which can cause changes in the network's topology [66]. As a result, the malleability of the network topology is seen as an obstacle to the routing process in such networks. In addition, one of the most significant challenges is the size of the network, which can be small or large depending on the current traffic conditions [87]. For instance, the scale of the network might be much smaller in rural areas as opposed to urban regions, highways, or metropolitan cities. This is because large urban areas have a greater population density [88]. If the network has been severely disrupted in any circumstance, it has the potential to become fragmented, which makes the role of routing particularly important. Because the IoV and IoFV are dependent on VANETs and FANETs, respectively, many researchers have \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Parameter** & **EC-MOPSO** & **MADCR** & **AFS Clustering** & **CACOIOV** & **MFCA-IoV** & **CAVDO** \\ \hline Grid size (km \(\times\) km) & 1 & 1, 2, 3, 4 & 1 & 4 & 1, 2, 3, 4 & 1, 2, 3, 4 \\ \hline Vehicles (count) & 50–100 & 30–60 & 30–60 & 40–200 & 30–60 & 30–60 \\ \hline Speed (km/h) & 30–100 & 72–108 & 30–50 & 79.2–108 & 79.2–108 & 79.2–108 \\ \hline Communication & 200 & Dynamic & 100–600 & Dynamic & Dynamic & Dynamic \\ \hline Mobility Model & Urban & Freeway & Urban & Freeway & Freeway & Freeway \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters of clustering experiments. Figure 5: Number of generated clusters by algorithms using freeway mobility model at various grid sizes with 40 vehicles in the network. implemented numerous traditional VANET/FANET protocols to solve the problem of routing in vehicular networks [42]. As a result of the fact that VANETs and FANETs are a subset of MANETs, a significant number of studies on the implementation of MANET routing protocols have been carried out. In addition to conventional routing algorithms, the usage of swarm intelligence-based approaches is frequent, as is the case with clustering. Table 5 summarizes such algorithms. There are numerous types of routing protocols that differ according to their application [89]. It is possible to categorize them according to several criteria [90], as indicated in the Figure 6.  
Unicast routing means the data are transmitted from one source node to another destination node [102]. Therefore, it can be marked as point-to-point communication [103]. An example of such a protocol is the F-Ant protocol [97] leveraging the hybrid system of fuzzy logic, as well as the principles of ant colony optimization to route data between two nodes. This system considers data packets as ants laying pheromones on routes. Coupling the evaporation of the pheromone and indicators such as the received signal strength metric, congestion metric, and bandwidth of the node helps the system determine  
Figure 6: Taxonomy of routing protocols used in vehicular networks. \begin{table}
\begin{tabular}{l c c c c c c} \hline
**Algorithm** & **WoS** & **Scopus** & **Year** & **Citations WoS** & **Citations Scopus** & **Reference** \\ \hline PSO & Yes & Yes & 2019 & 1 & 1 & [91] \\ \hline PSO & Yes & Yes & 2021 & 1 & 1 & [92] \\ \hline PSO & Yes & Yes & 2021 & 2 & 3 & [93] \\ \hline PSO & Yes & Yes & 2020 & 2 & 3 & [94] \\ \hline PSO & Yes & Yes & 2021 & 6 & 8 & [95] \\ \hline PSO & Yes & Yes & 2020 & 6 & 8 & [96] \\ \hline F-Ant & Yes & Yes & 2018 & 29 & 39 & [97] \\ \hline Glow-Worm Swarm & Yes & Yes & 2022 & 1 & 0 & [98] \\ \hline Glow-Worm Swarm & Yes & Yes & 2018 & 5 & 4 & [99] \\ \hline ACO & Yes & Yes & 2022 & 18 & 27 & [100] \\ \hline Firefly & Yes & Yes & 2019 & 6 & 5 & [101] \\ \hline \end{tabular}
\end{table} 
Table 5: Overview of swarm intelligence-based routing algorithms. the best path to a destination. F-Ant routing ensures the fulfillment of quality of service and low delay. On the contrary, this protocol may be vulnerable to security threats.