Task offloading and optimization methods in UAV-enabled mobile edge computing: A comprehensive survey

Abstract

The surge in compute-intensive and latency-sensitive applications such as autonomous driving and smart cities is straining traditional cloud computing. Recent advancements in Mobile Edge Computing (MEC) and the emergence of Beyond 5G (B5G) technologies have catalyzed the integration of Unmanned Aerial Vehicles (UAVs), heralding UAV-enabled MEC as a transformative paradigm. By utilizing UAVs as flexible aerial edge servers or relays, MEC can bring computational resources closer to end-users, thereby reducing latency and enhancing service quality. Despite their advantages, challenges remain regarding task offloading, specifically, determining when, how, and whether to transfer computational tasks from user devices to UAV-MEC servers for remote processing. UAV constraints, offloading strategies, communication channels, and energy consumption complicate the task offloading process. Therefore, sophisticated optimization strategies are essential to balance latency, energy consumption, task completion rates, and overall system efficiency. This survey paper focuses on task offloading strategies and optimization methods for UAV-enabled MEC networks. We discuss potential UAV-enabled MEC architectures, focusing on the interactions between UAVs operating within the MEC framework and ground users, as well as communication channel access. We present task offloading methods and optimization techniques. Although UAV-enabled MEC holds great promise, it still faces challenges that need to be addressed to support a broader range of applications. We highlight research issues and future opportunities, divided into three levels based on the real-world deployment of UAV-enabled MEC, moving from those most critical to those important for optimization and scale.

Keywords: Optimization, Mobile edge computing, Task offloading, UAV-enabled MEC