# VANET Clustering Based Routing Protocol Suitable for Deserts
## 3 System Model and Network Logical Structure  
The VANET scenarios adopted throughout this paper are in desert environments without ordinary communication facilities. This work considers a group of vehicles working on a specific mission. The proposed system model assumes the number of vehicles \((N)\), divided into different clusters according to their location, as shown in Figure 1. Each cluster \((C)\) has one CH and cluster members \((Cm)\). The number of vehicles within one cluster is \(N_{c}\) and the maximum number of vehicles for one cluster \(N_{max}\). The number of hops is \(M\), and the number of maximum allowable hops is \(M_{max}\). The primary communication distance of the normal VANET for two vehicles \(i\),\(j\) is \(L\), which can be expressed as:  
\[L=\sqrt{(x_{i}-x_{j})^{2}+(y_{i}-y_{j})^{2}} \tag{1}\]  
Here, \(x\), \(y\) represent the coordinates of the vehicles \(i\), \(j\) and \(L\leq L_{max}\), where \(L_{max}\) is the maximum communication distance between two vehicles that is determined according the PHY specification of the communication system. However, this communication distance can be extended using the multi-hop property of VANETs. This means, the maximum distance \(L_{max}\) can be obtained by:  
\[L_{max}=M_{max}L_{max} \tag{2}\]For a specific number of hops \(M\) using \(K\leq N_{c}\) vehicles, the communication distance can be obtained as:  
\[L_{M}=\sum_{m=1}^{M}L_{m} \tag{3}\]  
Here, \(L_{m}\) represents the communication distance of a specific hop \(m\).  
The direction, the location and speed information of the vehicles can be collected from the GPS of each vehicle. The vehicle periodically broadcasts this information using the _ad hoc_ communication. Vehicles within the coverage region store this information and forward it to the next hop together with their information. After a number of transmissions, each vehicle has the information of the other vehicles. In this paper, we assume that the vehicles are firstly classified into four groups according to their directions. We employed C4.5 [25] as a simple classification tree algorithm to classify the vehicles into four groups using a simple training set shown in Table 1. For each group, the clustering operation is performed, and the CH is accordingly elected.  
The designed model can be logically represented by three layers, which form the logical structure of the network, as shown in Figure 2. The bottom layer describes the communication network of the CH with its cluster members and the internal communication within the cluster. The middle layer, which acts as a network backbone, describes the communication between the cluster's heads. The upper layer is formed by the vehicles equipped with satellite and mobile links to outside networks.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Orientation** & **Class** \\ \hline \(316^{\circ}:45^{\circ}\) & East \\ \(46^{\circ}:135^{\circ}\) & North \\ \(136^{\circ}:225^{\circ}\) & West \\ \(226^{\circ}:315^{\circ}\) & South \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simple training set for vehicles’ direction classification used by C4.5.  
Figure 1: System model.