This paper presents a novel multi-tier UAV-assisted edge computing system designed for low-altitude networks. The system comprises vehicle users, lightweight Low-Tier UAVs (L-UAVs), and High-Tier UAV (H-UAV). L-UAVs function as small-scale edge servers positioned closer to vehicle users, while the H-UAV, equipped with more powerful server and larger-capacity battery, serves as mobile backup server to address the limitations in endurance and computing resources of L-UAVs. The primary objective is to minimize task execution delays while ensuring long-term energy stability for L-UAVs. To address this challenge, the problem is first decoupled into a series of deterministic problems for each time slot using Lyapunov optimization. The priorities of task delay and energy consumption for L-UAVs are adaptively adjusted based on real-time energy status. The optimization tasks include assignment of tasks, allocation of computing resources, and trajectory planning for both L-UAVs and H-UAV. Simulation results demonstrate that the proposed approach achieves a reduction of at least 26% in transmission energy for L-UAVs and exhibits superior energy stability compared to existing benchmarks.
@article{arxiv.2508.14601,
title = {Multi-Tier UAV Edge Computing for Low Altitude Networks Towards Long-Term Energy Stability},
author = {Yufei Ye and Shijian Gao and Xinhu Zheng and Liuqing Yang},
journal= {arXiv preprint arXiv:2508.14601},
year = {2025}
}