Mobile edge computing (MEC) is a promising technology to meet the increasing demands and computing limitations of complex Internet of Things (IoT) devices. However, implementing MEC in urban environments can be challenging due to factors like high device density, complex infrastructure, and limited network coverage. Network congestion and connectivity issues can adversely affect user satisfaction. Hence, in this article, we use unmanned aerial vehicle (UAV)-assisted collaborative MEC architecture to facilitate task offloading of IoT devices in urban environments. We utilize the combined capabilities of UAVs and ground edge servers (ESs) to maximize user satisfaction and thereby also maximize the service provider's (SP) profit. We design IoT task-offloading as joint IoT-UAV-ES association and UAV-network topology optimization problem. Due to NP-hard nature, we break the problem into two subproblems: offload strategy optimization and UAV topology optimization. We develop a Three-sided Matching with Size and Cyclic preference (TMSC) based task offloading algorithm to find stable association between IoTs, UAVs, and ESs to achieve system objective. We also propose a K-means based iterative algorithm to decide the minimum number of UAVs and their positions to provide offloading services to maximum IoTs in the system. Finally, we demonstrate the efficacy of the proposed task offloading scheme over benchmark schemes through simulation-based evaluation. The proposed scheme outperforms by 19%, 12%, and 25% on average in terms of percentage of served IoTs, average user satisfaction, and SP profit, respectively, with 25% lesser UAVs, making it an effective solution to support IoT task requirements in urban environments using UAV-assisted MEC architecture.

翻译：移动边缘计算（MEC）是一项有望满足复杂物联网设备日益增长的需求并克服其计算限制的技术。然而，在城市环境中部署MEC可能面临挑战，原因包括高设备密度、复杂的基础设施以及有限的网络覆盖。网络拥塞和连接问题可能对用户满意度产生不利影响。因此，本文采用无人机辅助的协同MEC架构，以促进城市环境中物联网设备的任务卸载。我们利用无人机与地面边缘服务器的综合能力，旨在最大化用户满意度，从而也最大化服务提供商的利润。我们将物联网任务卸载设计为一个联合的物联网-无人机-边缘服务器关联与无人机网络拓扑优化问题。鉴于其NP难特性，我们将该问题分解为两个子问题：卸载策略优化和无人机拓扑优化。我们开发了一种基于带规模与循环偏好的三边匹配的任务卸载算法，以在物联网设备、无人机和边缘服务器之间建立稳定的关联，从而实现系统目标。我们还提出了一种基于K-means的迭代算法，用于确定所需无人机的最小数量及其部署位置，以便为系统中尽可能多的物联网设备提供卸载服务。最后，我们通过基于仿真的评估，证明了所提出的任务卸载方案相较于基准方案的有效性。该方案在服务的物联网设备比例、平均用户满意度和服务提供商利润方面分别平均优于基准方案19%、12%和25%，同时所需无人机数量减少25%，这使其成为利用无人机辅助MEC架构支持城市环境中物联网任务需求的有效解决方案。