# TRUST-ME: Trust-Based Resource Allocation and Server Selection in Multi-Access Edge Computing
## 1 Introduction
### Related Work and Motivation
An iterative algorithm that minimizes the latency in digital twin-aided MEC systems for industrial IoT was formulated in [19] to optimize the resource allocation through an alternating optimization approach combined with an inner convex approximation. A game-theoretic approach to optimize multiuser computing task offloading in a device enhanced MEC environment is presented in [20] with the aim to maximizing the aggregate offloading benefits by jointly optimizing the heterogeneous (i.e., communication and computing) resource allocation. A similar approach was followed in [21] based on a multistage Stackelberg game, where in the first stage, a contract-based incentive mechanism was introduced to motivate the IoT nodes to share idle resources, and in the second stage, the optimal pricing strategies are derived to optimize the IoT nodes' utilities. Moreover, an energy-efficient game-theoretic approach to support the resource allocation for the IoT nodes in a MEC environment is proposed in [22] with the aim to optimize the IoT nodes' task offloading decisions based on their personal latency and energy consumption constraints. A similar decentralized game-theoretic approach was followed in [23] with the aim to optimally allocate mobile devices to nearby MEC servers to minimize the latency and communication interference of the IoT devices. The latter approach was further extended in [24] to address the interference-aware software-as-a-service user allocation problem in MEC environments by demonstrating the effectiveness and efficiency of a decentralized algorithm to find its Nash equilibrium. A decentralized-edge-node-grouping algorithm in MEC networks was investigated in [25] by addressing the system uncertainties and proving the convergence to a unique Nash equilibrium with high performance. A fair and efficient mechanism for multi-resource allocation in MEC environments was studied in [26] and guarantees a Nash equilibrium in polynomial time and satisfies various fairness criteria. Given the distributed nature of the decision-making process regarding the resource allocation in MEC environments, the principles of _reinforcement learning (RL)_ have been widely adopted in the recent literature [27]. A deep-reinforcement-learning-based approach to optimize computation offloading and resource allocation in NOMA-MEC systems is proposed in [28] in order to significantly reduce the computational overhead. The authors in [29] integrated artificial intelligence into UAV data offloading in a multi-server MEC environment using game theory and reinforcement learning principles to enhance the efficiency and effectiveness. Various optimization algorithms for managing resources in a MEC environment were studied in [30], and the authors demonstrated their similar performances and highlighted the adaptability of deep-RL-based approaches to changing conditions. A software-defined network or SDN-based MEC system is introduced in [31] using a stochastic game-based resource allocation algorithm to effectively reduce energy consumption and processing latency in IoT systems. A novel AI-based mechanism using RL and Bayesian reasoning for autonomous MEC server activation was analyzed in [32] to enhance the system's reputation and manage computing costs while ensuring the users' quality of experience. An RL-based strategy for efficient task offloading and resource allocation in multi-edge server collaboration is presented in [33] to jointly optimize the task latency, the user service numbers, and the energy consumption. An RL-based approach and an enhanced deferred acceptance algorithm are discussed in [34] to jointly optimize the virtual network functions' (VNFs') placement, routing, and scheduling, and significantly reduce the service latency and resource consumption in MEC environments. A strategy for joint task type and vehicle speed-aware task offloading and resource allocation in vehicular MEC environments is proposed in [35] to optimize the energy costs and processing revenue while meeting the task delay constraints.