## 3 Public Transport-Assisted Data-Dissemination System
### Multi-Attribute Decision Making
#### 3.1.3. Madm  
This step helps with deciding between different networks, based on the weights obtained from the decision matrix, the alternatives, and different criteria. * Analytical Hierarchical Process  
The analytical Hierarchical process (AHP) method is a multi-criteria decision-making process for network selection. It was developed at the Wharton School of Business by [PERSON] in the 1970s [38]. AHP works on the function of priority and rank to evaluate subjective weights to achieve the specified goals. We have used this process to select a best-featured network from the given alternatives for the given service class based on the following criteria--Energy Consumption, Bandwidth, Delay, and Delivery Probability. We have also used this process for choosing a priority of network types for each data type. Network weighing is an important factor to characterize the network performance and user's preferences. We use the hierarchy analysis method to allocate the appropriate weight to each selection metric. We further categorize traditional networks into WLAN and UMTS networks for impartial scheming with different attributes, as shown in Figure 9. The logical flowchart of the AHP algorithm considers the hierarchical structure with the main goal, multiple criteria, and network alternatives to select. We have defined utility functions for all the attributes for a network assessment. A user's preference will be based on multiple criteria for network selection. We assume that WLAN users have wireless access to their system, but with a fixed location--or we can say a local network--and that they use all their devices to avail the services and disseminate data to nearby RSUs for further transmission. However, they have good speed and bandwidth values. On the other hand, UMTS is a mobile cellular device and can roam around with their data plans, but with limited bandwidths and larger delays as per the delivery probability and data network's range. 1. Subdivide a problem into further sub-problems by defining an objective function, criteria, and possible alternatives. Here, the objective is our goal of achieving optimal network selection. The multiple criteria are the factors affecting the preference for selection. 2. Develop the hierarchy model of all objectives along with their elements to obtain the priorities of criteria through pairwise comparison matrices. Figure 9: AHP for network selection. 3. Construct a pairwise comparison matrix for each criterion of hierarchical structure in such a way that all associated criteria are compared with each other as per the intensity of importance [39], with respect to the scale. We believe that a pairwise comparison between alternatives helps for qualitative judgment. This qualitative pairwise comparison follows the importance scale, as shown in Table 1. \[P= \begin{pmatrix}C_{1}&C_{2}&\cdots&C_{j}&\cdots&C_{N}\\ 1&x_{12}&\cdots&x_{1,j}&\cdots&x_{1,N}\\ x_{2,1}&1&\cdots&x_{2,j}&\cdots&x_{2,N}\\ \vdots&\vdots&1&\vdots&\ddots&\vdots\\ x_{i,1}&x_{i,2}&\cdots&1&\cdots&x_{i,N}\\ \vdots&\vdots&\ddots&\vdots&1&\vdots\\ x_{M,1}&x_{M,2}&\cdots&x_{M,j}&\cdots&1\end{pmatrix}\begin{pmatrix}C_{1}\\ C_{2}\\ \vdots\\ C_{i}\\ \vdots\\ \vdots\\ \vdots\\ \end{pmatrix} \] (7)
4. Perform the normalization of a given matrix \(P\), which is now denoted as \(P_{Norm}\): \[P_{Norm}= \begin{pmatrix}C_{1}&C_{2}&\cdots&C_{j}&\cdots&C_{N}\\ 1&z_{1,2}&\cdots&z_{1,j}&\cdots&z_{1,N}&C_{1}\\ z_{2,1}&1&\cdots&z_{2,j}&\cdots&z_{2,N}&C_{2}\\ \vdots&\vdots&1&\vdots&\ddots&\vdots&\vdots\\ z_{i,1}&z_{i,2}&\cdots&1&\cdots&z_{i,N}&C_{i}\\ \vdots&\vdots&\ddots&\vdots&1&\vdots&\vdots\\ x_{M,1}&z_{M,2}&\cdots&z_{M,j}&\cdots&1\end{pmatrix}\begin{pmatrix}\text{ where},z_{i,j}\\ \sum_{i=1}^{N}x_{i,j}\end{pmatrix} .\] (8)
5. The contributions of each normalized metric are multiplied by the assigned importance weight wj, and can be calculated for the ith criteria, as below: \[P_{w}=\frac{\sum_{i=1}^{N}Z_{i,j}}{N}\text{with}\sum_{i=1}^{N}P_{w}=1,\] (10) such that \(P_{w}\) is the weight vector. 6.