## 4 Numerical Analysis and Results
### Network Selection for Different Services  
We have discussed the AHP procedure and utility theory for all the attributes' weighings and preferences. Now, the AHP procedure will help us weigh different attributes for all of our services. In our work, we define the traditional and vehicular networks as alternatives to choose from and the available list is \(I_{an}=(W,U,V)\). Algorithm 1 illustrates the whole process for optimal network selection based upon different services. There is a list of available networks \(I_{an}=(W,U,V)\) to choose from. We collect all the network attributes in list \(I_{an}=a_{1},a_{2}.....a_{n}\), named energy-efficient \(e_{u}\), delivery probability \(dp_{u}\), delay demand \(d_{u}\), and available bandwidth \(b_{i}\) of both networks. Then, we follow all the steps to rank the network among all the networks, as per different services. We use this network-selection technique only to offer the best option as per their requirements to maintain QoS. It is mandatory to pass the consistency check in AHP in order to obtain an accurate judgment matrix. If any of the matrices fail to pass this check, the user will have to give preferences to the design matrix. We will first analyze public vehicle distributions near bus stops to know the availability of networks to choose from, and then further evaluate the performance of all networks for different services. Figure 12 illustrates the criteria weights given to all the attributes as per different services. For example, as discussed before, Service 1 has delay-tolerant features and will be considered an energy-efficient data-dissemination network. Therefore, the criteria weight will be allocated more heavily on the EE attribute. In such a way, all the weights are distributed as per the service profile. The priority vector is calculated for all the services as per different attributes as shown in Figure 13. The final score is calculated as discussed in Figure 14. Utility functions are defined already for all the attributes. For all of these services, we will have different utility values. We will score our network based upon the maximum utility value for all the services. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Attributes** & \begin{tabular}{c} **Energy** \\ **Efficiency** \\ \end{tabular} & **Bandwidth** & \begin{tabular}{c} **Delay** \\ **Tolerance** \\ \end{tabular} & \begin{tabular}{c} **Delivery** \\ **Probability** \\ \end{tabular} & \begin{tabular}{c} **Criteria** \\ **Weight** \\ \end{tabular} \\ \hline \begin{tabular}{c} **Energy** \\ **Efficiency** \\ \end{tabular} & 1 & 1/6 & 1/6 & 1/7 & 0.05355183 \\ \hline \
\begin{tabular}{c} **Bandwidth** \\ **Tolerance** \\ \end{tabular} & 6 & 1/3 & 1 & 1/5 & 0.15369319 \\ \hline
\begin{tabular}{c} **Delivery** \\ **probability** \\ \end{tabular} & 7 & 1 & 5 & 1 & 0.4540202 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Normalized score table for all the attributes with the weight factor. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Attributes** & \begin{tabular}{c} **Energy** \\ **Efficiency** \\ \end{tabular} & **Bandwidth** & \begin{tabular}{c} **Delay** \\ **Tolerance** \\ \end{tabular} & \begin{tabular}{c} **Delivery** \\ **Probability** \\ \end{tabular} & \begin{tabular}{c} **Criteria** \\ **Weight** \\ \end{tabular} \\ \hline \begin{tabular}{c} **Energy** \\ **Efficiency** \\ \end{tabular} & 1 & 1/6 & 1/6 & 1/7 & 0.05355183 \\ \hline \
\begin{tabular}{c} **Bandwidth** \\ **Tolerance** \\ \end{tabular} & 6 & 1/3 & 1 & 1/5 & 0.15369319 & 0.1499 \\ \hline
\begin{tabular}{c} **Delivery** \\ **probability** \\ \end{tabular} & 7 & 1 & 5 & 1 & 0.45402002 & 0.4429 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Normalized score table with priority vector ```
Input : Different services as per user's profile: energy efficient \(e_{u}\), delivery probability \(dp_{u}\), delay demand \(d_{u}\), available bandwidth \(b_{i}\) of both networks, available network list \(I_{an}\).