Multi-Tier UAV Edge Computing for Low Altitude Networks Towards Long-Term Energy Stability

2025-08-20

AI Summary

To address the challenge of jointly optimizing energy efficiency and task latency for lightweight low-altitude unmanned aerial vehicles (L-UAVs) in low-altitude networks, this paper proposes a multi-tier UAV-enabled mobile edge computing architecture: L-UAVs serve vehicular users locally, while high-altitude UAVs act as mobile backup servers. The framework jointly optimizes task offloading, computational resource allocation, and three-dimensional trajectory planning. Leveraging the Lyapunov optimization framework, we decompose the stochastic long-term constraint problem into deterministic per-slot subproblems, enabling adaptive co-control of task scheduling and energy state evolution. Experimental results demonstrate that the proposed scheme guarantees end-to-end latency ≤50 ms while reducing L-UAV transmission energy consumption by ≥26%. To the best of our knowledge, this is the first work achieving both millisecond-level responsiveness and long-term energy stability in low-altitude networks, significantly outperforming existing baseline approaches.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• Minimizing task execution delays in UAV edge computing networks
• Ensuring long-term energy stability for low-tier UAV servers
• Optimizing resource allocation and trajectory planning dynamically

Innovation

• Multi-tier UAV edge computing system
• Lyapunov optimization for energy stability
• Adaptive task and resource allocation