# TRUST-ME: Trust-Based Resource Allocation and Server Selection in Multi-Access Edge Computing
## 1 Introduction
### Related Work and Motivation  
Several recent research works focused on the problem of _resource allocation and/or server selection_ in MEC environments. A non-orthogonal multiple access or NOMA-based vehicle edge computing network model was designed in [3] and consists of several heuristic algorithms to jointly optimize the task offloading and MEC resource assignment to minimize the overall system's costs while ensuring delay tolerance for vehicular user equipment. An efficient D2D-assisted MEC computation offloading framework based on attention communication deep reinforcement learning (ACDRL) is introduced in [4] to enhance a system's performance by facilitating resource sharing and reducing the latency. An efficient algorithm for optimizing the energy consumption in an NOMA-enabled MEC system with strict latency requirements is designed in [5] to address both the resource allocation and the subchannel assignment through decomposition and optimization methods. A similar approach is followed in [6] by focusing on the energy-efficient and low-latency task execution for IoT devices through the joint sparse code multiple access (SCMA) codebook assignment, power allocation, CPU frequency scheduling, and task offloading policy optimization. _Optimization techniques and heuristics algorithms_ were designed to address the problem of resource allocation and/or server selection in MEC environments [7]. The joint task offloading and resource allocation problem in cooperative MEC server scenarios was studied in [8] by targeting the efficiency and fairness of the resource allocation process through a two-level algorithmic approach. A computationally efficient repeated auction model is proposed in [9] by utilizing a modified generalized second price algorithm to manage the resource allocation in the competitive MEC offloading service market while considering varying server workloads. Given the plurality of IoT devices in several applications supported by unmanned aerial vehicles (UAVs), an innovative alternative optimization approach for integrating UAV systems as both remote base stations and mobile edge servers using NOMA is proposed in [10] to minimize the latency and optimize the resource allocation. An MEC framework for IoT applications in air-ground-integrated wireless networks was analyzed in [11] with the aim to minimize the service latency while meeting the learning accuracy requirements and reducing the energy consumption through an optimized deep neural network (DNN) model decision, computation, communication resource allocation, and UAV trajectory control. A collaborative multi-UAV-assisted MEC system integrated with a terrestrial base station is proposed in [12] in order to minimize the total latency for IoT devices by optimizing the offloading decisions and resource allocations under the IoT devices' energy constraints. An MEC framework that jointly optimizes the caching, offloading, deployment, and resource allocation was analyzed in [13] to minimize the latency and energy costs. _Game theory_ has been extensively adopted in the recent literature to deal with the distributed optimization problem of resource allocation in MEC environments [14]. A task-offloading scheme for MEC environments is proposed in [15] to optimize the users' utility by leveraging potential games. A scheme based on the double deep Q-network (DDQN) to optimize service caching, resource allocation, and computation offloading in a MEC system is analyzed in [16] to reduce the delay and energy consumption costs. Focusing on the MEC system's effective resource utilization, the authors in [17] introduce a joint bandwidth and computing frequency allocation mechanism based on the Stackelberg games. A two-level bargaining-based incentive mechanism for task offloading and collaborative computing in MEC-enabled networks was analyzed in [18] to ensure the optimal resource allocation and load balance.