## 4 Results and Analysis
### Mean Trust Value  
The mean trust value in a session is calculated for all GTS-requesting nodes that have been successfully allocated GTSs. The results are calculated for varying numbers of nodes, for varying bandwidth, and for varying transmission power, as shown in Figures 14, 15 and 16, respectively. Figure 14: Mean trust value calculated for varying GTS-requesting nodes. Figure 13: Transmission time of legitimate nodes for varying transmitting power. Figure 14 shows that the means trust value of all the successfully allocated nodes in a session in the proposed \(TMPAD\) is higher than the other three standards for varying numbers of nodes. It is due to the fact that \(TMPAD\) scrutinizes GTS-requesting nodes in transmitting their data by applying TOPSIS, and trust calculation is one of the important parameters in calculating the TOPSIS value. The results show that with the increase in the number of GTS-requesting nodes, the mean trust value of all GTS-allocating nodes remains similar; however, it is more than the other three standards. Figure 16: Mean trust value of GTS-requesting nodes for varying transmitting power. Figure 15: Mean trust value of GTS-requesting nodes for varying bandwidth capacity. Results in Figures 15 and 16 further show that the mean trust value of all successfully allocated GTS-requesting nodes in \(TMPAD\) is higher than the other three algorithms for varying channel bandwidth and for varying transmission power, respectively. Figure 15 shows that the mean trust value calculated for increasing channel bandwidth is 0.64, whereas mean trust values in all GTS-allocated nodes of all three algorithms are about 0.5. It means that \(TMPAD\) assists the fog node in scrutinizing GTS-requesting nodes with higher calculated trust values. However, all other algorithms do not scrutinize GTS-requesting nodes based on their trust values and, consequently, their mean trust value is less than the proposed scheme. The same trend is observed for varying transmission power, as shown in Figure 16. It is evident from the results that the mean trust value calculated in \(TMPAD\) is significantly greater than the other three schemes, and up to 33% more mean trust value is achieved because \(TMPAD\) allows fog nodes to scrutinize nodes with higher trust values. However, the other three standards allocate GTS without considering their trust values, and some malicious nodes may also be entertained. In the end, we evaluated the performance of our proposed scheme in different prospects for varying trust threshold values, as discussed in Algorithm 1. Figure 17 comprises four subfigures, showing transmitted data of legitimate nodes, the number of GTS-requesting nodes entertained, the data transmission time of all the legitimate nodes, and the mean trust values. The results are compared with the 0/1 knapsack algorithm [32] in addition to the other three standards. The results show that the proposed scheme allows more legitimate nodes to transmit their data as compared to the four other schemes. In addition, it allows more data to be transmitted by all legitimate nodes to the fog computing node. Another subplot shows that the time to transmit data by all the selected legitimate nodes is more than the other four schemes. This is due to the reason that the transmission time of only the successfully allocated nodes is calculated, and TMPAD allows more nodes to transmit their data as compared to the other four competing algorithms. The fourth subplot evaluates the mean trust value of the selected nodes that are allowed to transmit their data. The  
that \(TMPAD\) allows 110% up to 335% more data transmission in a session than the well-known standards of FCFS, SJF, and LJF for varying numbers of nodes, varying channel bandwidth, and varying transmission power. Furthermore, the proposed scheme entertains up to 188% more GTS-requesting nodes to transmit their data in a session, as compared to the other algorithms.