Enhancing QoE in Large-Scale U-MEC Networks via Joint Optimization of Task Offloading and UAV Trajectories - He, Huaiwen; Yang, Xiangdong; Huang, Feng; Shen, Hong; Tian, Hui (IEEE internet of things journal, 2024)
Unmanned aerial vehicles (UAVs) have emerged as crucial components in advancing mobile edge computing (MEC), leveraging their proximity to edge nodes and scalable nature. This synergy holds significant promise within the Internet of Things (IoT) and Beyond 5G (B5G) domains. In this article, we concentrate on optimizing the shrinking ratio, a Quality of Experience (QoE) metric, within large-scale IoT networks empowered by UAV-enhanced MEC via joint optimizing task offloading, resource allocation, and UAV trajectories. This joint optimization problem presents significant challenges due to the intertwined nature of multiuser computing mode selection and strong coupling between user equipments (UEs) waiting time and UAV trajectory. To tackle these challenges, we formulate the problem as a mixed integer nonlinear programming (MINLP) problem and propose an iterative algorithm named BTOU by decomposing the original problem into two subproblems using the block coordinate descent (BCD) framework. For the task offloading and resource allocation subproblem, we present two algorithms: one employs a low-complexity greedy game-theoretic approach suitable for a large number of UEs, while the other leverages the penalty successive convex approximation (PSCA) technique along with first-order Taylor expansion approximation to achieve high-solution quality. For the UAV trajectory planning subproblem, we transform it into a Miller-Tucker-Zemlin (MTZ) model and devise a solution strategy. Extensive simulation results validate the effectiveness of our proposed algorithm, showcasing rapid convergence and a notable improvement in QoE of over 10% compared to benchmark methods.