# TRUST-ME: Trust-Based Resource Allocation and Server Selection in Multi-Access Edge Computing
## 1 Introduction
### Related Work and Motivation
A Q-learning-based reinforcement learning solution for optimizing dynamic resource allocation in 5G radio access network slicing supporting MEC systems is presented in [36] by significantly improving the network utility compared with myopic, random, and first-come-first-served approaches. A distributed framework using autonomous MEC servers and stochastic learning automata in IoT networks to optimize server activation, device-server associations, and transmission power for QoS satisfaction is presented in [37]. Additionally, a Markov-decision-process-based task offloading strategy in a vehicular edge-cloud computing network is introduced in [38] based on a dueling actor-critic reinforcement learning approach to effectively manage the resource allocation and minimize the task completion times. Similarly, a shared offloading strategy using deep reinforcement learning learning (DRL) for vehicular edge computing (VEC) is discussed in [39] to reduce the delay and energy consumption by allowing vehicles to share computing results and adaptively select optimal offloading modes in complex IoT environments. Also, a hierarchical vehicular edge computing architecture is designed in [40] to enhance the global task computing efficiency by allowing neighboring regions to share idle resources. The authors of [41] analyzed and compared value-based, policy-based, and hybrid deep reinforcement learning algorithms in order to address the task-offloading challenges in edge computing environments, and they provided a thorough discussion regarding their respective strengths, weaknesses, and future research directions. A dynamic offloading decision algorithm combined with DRL was analyzed in [42] to optimize the task offloading and resource allocation in an MEC environment, and it significantly improved the energy efficiency and task completion time under varying wireless conditions. A novel offloading algorithm that combines meta-reinforcement learning with DRL is proposed in [43] to efficiently generate near-optimal task-offloading decisions in resource-constrained IoT environments. A DRL-based computation offloading scheme for virtual reality video transmission in MEC networks was analyzed in [44] in order to optimize the task management through a deep deterministic policy gradient algorithm. Similarly, a DRL-based computation-offloading scheme for UAV-enabled MEC environments is proposed in [45] with the aim to reduce the task execution delay, energy consumption, and overall offloading cost compared with conventional methods. Moreover, an autonomous partial offloading system for delay-sensitive tasks in multiuser IoT MEC environments is introduced in [46] and is based on Q-learning and deep deterministic policy gradient strategies to significantly reduce the devices' delay and enhance their quality of service. A meta-RL task-offloading algorithm was designed in [47] based on graph neural networks and enhances the adaptability and robustness in dynamic MEC environments by effectively managing the task dependencies and optimizing the sampling efficiency.