# TRUST-ME: Trust-Based Resource Allocation and Server Selection in Multi-Access Edge Computing
## 1 Introduction
### Contributions  
Though significant research efforts have been devoted in the recent literature to deal with the joint problem of MEC server selection and resource allocation in MEC environments consisting of multiple IoT devices, the study of these problems remains highly fragmented, meaning that the recent research works either focus on the problem of MEC server selection or on the problem of resource allocation in a separate manner. Additionally, assessing the trustworthiness of the MEC servers' computing capabilities in order to meet the IoT devices' latency constraints has been largely overlooked. To address these research gaps, we propose the TRUST-ME model, which enables the autonomous selection of edge servers by the IoT devices for task offloading while considering the MEC servers' trustworthiness in delivering quality of service to the IoT devices. Furthermore, we introduce a distributed resource allocation framework based on the multilateral bargaining games to facilitate the fair allocation of the MEC servers' computing resources to the IoT devices based on the computing demands of the latter ones.  
The main contributions of this research work are summarized below:  
1. Initially, the TRUST-ME model is presented as consisting of multiple edge servers and multiple IoT devices, where the latter ones are characterized by different computing tasks to be fully offloaded to the MEC servers for further processing. The communications and computing characteristics of the IoT devices are presented and a utility function is designed to capture the IoT devices' benefit by the experienced latency and cost from utilizing the computing capacity of the selected MEC server.
2. A novel trust model of the IoT devices to the MEC servers' computing capabilities was designed and consisted of the direct and indirect trust of the devices, where the latter one was derived from the social ties between the IoT devices that have used the same MEC server to process their computing tasks. A reinforcement learning approach based on optimistic Q-learning with an upper bound confidence action selection algorithm is presented to enable the IoT devices to autonomously select an MEC server.
3. A multilateral bargaining model is presented for resource allocation, enabling the MEC servers to allocate their computing capacity to the IoT devices' tasks by taking into account their computing demand and the fairness in service provision between the devices.
4. A detailed set of simulation-based experiments was performed to demonstrate the operational efficacy and performance convergence of the TRUST-ME model in terms of the MEC server selection and resource allocation. Moreover, a real-world scenario was analyzed by considering different types of computing applications requested by the IoT devices to demonstrate the TRUST-ME model's applicability. A thorough scalability analysis also quantified its efficiency and robustness. A detailed comparative evaluation against alternative MEC server selection and resource allocation approaches quantified the superiority of the TRUST-ME model over current state-of-the-art methods.