# TRUST-ME: Trust-Based Resource Allocation and Server Selection in Multi-Access Edge Computing
## 5 Numerical Evaluation
### Comparative Evaluation  
As discussed in this section, we performed a comparative analysis of the proposed TRUST-ME model against two alternative scenarios:  
* **Without trust:** the users selected an MEC server without considering the trust levels related to the services provided, and the resource allocation followed the multilateral bargaining game. Figure 5: (**a**) Convergence of the OQ-UCB reward for the final action selected by users with high, medium, and low computing demands. (**b**) Average computing capacity allocated to users by the selected MEC servers. Figure 6: Scalability analysis. * **Proportional fair:** the users selected an MEC server using the proposed OQ-UCB algorithm, and the MEC servers' resources were allocated based on the proportional fairness by taking into account the users' data-processing needs, i.e., \(B_{n}\). Figure 7a-c show the percentage of the computing capacity allocated by the selected MEC servers to the three user groups (high, medium, and low computing demand), where in total, we considered a set of 12 users. Figure 8a-c show the average computing capacity allocation, the experienced average users' utility, and the corresponding latency experienced by users under each scenario. The results reveal that the proposed TRUST-ME model achieved a more balanced distribution of the users across the MEC servers. The users with higher computing demands tended to select the MEC servers with lower capacities in order to dominate them and be allocated a large portion of their computing capacity. In the without trust scenario, the users with high computing demands predominantly chose the server with the medium computing capacity (MEC server 2), leaving the users with lower computing demands to utilize the higher-capacity server (Figure 7b). In the proportional fair scenario, the users with higher computing demands often selected the MEC servers with the high computing capacity, allowing them to dominate these servers and secure a larger share of its resources. Thus, the users with lower computing demands were distributed between the servers to capture their remaining computing capacities. The results also show that the without trust and proportional fairness resource allocation scenarios inadequately addressed the users' computing demands, where they experienced higher latencies due to insufficient and unbalanced resource allocation (Figure 8c). The proportional fairness (PF) strategy, while it allocated the resources based on the users' data processing needs, tended to allow users with high computing demands to dominate high-capacity servers. This approach can result in suboptimal resource utilization, where low-demand users do not receive sufficient resources. Also, the PF strategy did not account for the dynamic and often fluctuating network conditions as effectively as TRUST-ME. The users could experience varying levels of service quality due to the bandwidth fluctuations, server loads, and channel gain conditions. Moreover, it should be noted that  
Figure 8: Comparative evaluation showing the (**a**) average computing capacity allocation, (**b**) average users’ utility, and (**c**) latency experienced by users across different scenarios. Figure 7: Comparative heat distribution plots showing the percentage allocations of the computing capacity by the selected MEC servers to high, medium, and low computing demand user groups. although the latency was only slightly higher by following the PF strategy, this approach did not fully address the need for adaptive and efficient resource allocation under varying demands and conditions. Additionally, the users with higher demands experienced better performance at the expense of those with lower demands under the PF strategy. On the other hand, the TRUST-ME model provided a more robust evaluation of the MEC server performance by jointly considering the direct and indirect trust. This adaptability ensured consistent and reliable service quality, even under fluctuating network conditions.