关键词: 移动边缘计算, 无人机群, 任务卸载, 负载均衡, 航迹规划, 集群并行计算

Abstract: Unmanned Aerial Vehicle (UAV) swarm, integrated with 5G networks, serves as a fleet of flying tools carrying computational resources, providing additional computational power support for Mobile Edge Computing (MEC) networks. In semi-connected networks, where there are challenges such as scarce infrastructure computing resources, massive task data, uneven distribution of mobile Internet of Things (IoT) devices, and complex communication scenarios using Orthogonal Frequency Division Multiple Access (OFDMA) technology, a Moving-aware Adaptive-parallel Task Offloading System (MATOS) was proposed. The system was comprised of a ground equipment layer, a UAV layer, and an edge computing layer, aiming to reduce task offloading latency and energy consumption, thus enhancing the task offloading success rate. The UAV swarm was utilized as Air Base Stations (ABS) to handle task offloading and relay services. Firstly, to improve the task transmission quality between ground devices and the UAV swarm, a task collaborative collection mechanism was proposed, which combined task attributes with mobile perception of regional service devices. Secondly, an Adaptive Parallel Genetic Ant Colony Optimization (AGACO) task offloading mechanism was proposed, which integrated UAV swarm trajectory planning to achieve load balancing for ABS and reduce task offloading latency. Finally, by jointly optimizing UAV swarm trajectory planning, task offloading latency, and task offloading energy consumption, the task offloading success rate was improved. Experimental results show that MATOS reduces flight energy consumption by 40% compares to the energy efficient edge Cloud Architecture (RESERVE), a Smart and Trusted Multi-UAV Task Offloading system (STMTO), UAV Edge Computing IoT Network (UECIN), Multi-UAV Assisted Offload System (MAOS), and Mobility-aware Online Task Offloading (MOTO). Compared with RESERVE, the task unloading delay is reduced by 38%, and the task unloading energy consumption is reduced by 31%, which verifies the superiority of MATOS. 

Key words: mobile edge computing, Unmanned Aerial Vehicle (UAV) swarm, task offloading, load balancing, track planning, cluster parallel computing