# TRUST-ME: Trust-Based Resource Allocation and Server Selection in Multi-Access Edge Computing
## 5 Numerical Evaluation
### Operation and Performance of the TRUST-ME Model  
For this section, our goal was to present the pure operation and performance of the TRUST-ME model. Specifically, we considered an indicative setup that consisted of 12 users and three MEC servers, where the users with higher IDs were characterized by relaxed latency constraints, as they requested the processing of a smaller amount of data and imposed a smaller constraint in terms of the expected achieved utility. Figure 2a-d demonstrate the users' allocated computing capacity, their achieved data rate, and their experienced latency and utility while considering the selected MEC server following the optimistic Q-learning with the upper bound confidence action selection reinforcement learning algorithm. The results demonstrate that more users tended to select the MEC server that was characterized by a higher computing capacity, i.e., MEC servers with lower IDs. Furthermore, the results show that the users with lower IDs that entered into the the bargaining process early and are characterized by a computing task that requires higher computing capacity resulted in being allocated a corresponding higher computing capacity  
Figure 1: TRUST-ME flowchart.  
by the selected MEC server. Furthermore, considering that the users resided relatively closer to the MEC server with higher computing capacity, they achieved a corresponding higher data rate, while also considering that users with lower IDs were in general closer to the MEC servers. By combining the two previous observations, we concluded that the users with lower IDs experienced lower corresponding latency by considering the selected MEC server, as they were allocated a higher computing capacity and they achieved a better data rate. The latter observation resulted in higher experienced utility for users with lower IDs relative to the corresponding selected MEC server given that the users who selected the same MEC server competed for its computing resources. Furthermore, Figure 3 illustrates the number of users associated with each MEC server and the corresponding total trust score accumulated by all the users who selected the corresponding MEC server. The results indicate a clear preference among the users for selecting the MEC server with a greater computing capacity. This preference correlated with an increased trust score for the service given the superior quality of service experienced when using higher-capacity MEC servers.  
Figure 2: (**a**) Allocated computing capacity, (**b**) data rate, (**c**) experienced latency, and (**d**) experienced utility for the users and their respective MEC selected server.  
The convergence of the optimistic Q-learning with the upper bound confidence action selection reinforcement learning algorithm was also studied. Figure 4a,b demonstrate the OQ-UCB reward (Equation (15)) and the corresponding experience expected reward (Equation (16)) for all the users regarding their corresponding finally selected action over the iterations of the OQ-UCB algorithm, where different colors are used for the corresponding finally selected MEC servers by each user. The results demonstrate that the OQ-UCB algorithm converged fast (in less than 200 iterations, corresponding to a few milliseconds) to a stable action selection and the corresponding expected reward, i.e., Q-value, achieved values above 95% for all the users. Moreover, the results show that the users with lower IDs experienced a higher OQ-UCB reward given the higher trust that they created with respect to the corresponding MEC server, their proximity to the MEC servers, and the higher computing capacity that they were allocated by the selected MEC server.