Joint Offloading and Resource Allocation Optimisation for UAV-Aided Low-Altitude Economy MEC System

In recent years, the development of $5 \mathrm{G} / 6 \mathrm{G}$ technologies has significantly heightened the demand for lowlatency and low-energy solutions in telematics scenarios. However, the limited computing power of IoT devices places them under tremendous pressure when handling computationally intensive tasks. Context, this paper considers an Unmanned aerial vehicle (UAV) assisted access edge computing (MEC) architecture for the Internet of Things (IoT), including the IoT device layer, the UAV edge server layer, and the base station (BS) layer. For dynamic environments and diverse computational requirements across time slots, we propose a low-complexity optimisation algorithm to solve the latency minimisation problem for task execution. The solution jointly optimizes the task offloading decision, communication resource allocation, and computational resources while satisfying multiple constraints such as latency, offloading method, and computational resource limitations of UAVs and BSs, with the goal of minimizing the total execution latency of the effectiveness of the proposed algorithm in terms of convergence behavior and the impact of various system parameters.