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

2026-02-04

AI Summary

This work addresses the challenge of jointly minimizing task latency and ensuring long-term energy stability for lightweight unmanned aerial vehicles (UAVs) acting as edge servers in low-altitude edge computing, particularly under uncertain future system dynamics. To this end, the authors propose a hierarchical dual-layer UAV collaboration architecture integrated with a Lyapunov optimization framework, which dynamically balances latency and energy consumption while jointly optimizing task offloading, computational resource allocation, and trajectory control. A novel adaptive mechanism driven by real-time energy states is introduced to simultaneously guarantee task processing performance and energy stability—a first in this domain. Extensive simulations demonstrate that the proposed approach reduces transmission energy consumption of lower-layer UAVs by over 26% and achieves significantly improved energy stability compared to existing baseline methods.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Learning on the Edge & Model Compression

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: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The agile mobility of Unmanned Aerial Vehicles (UAVs) makes them ideal for low-altitude edge computing. This paper proposes a novel multi-tier UAV edge computing system where lightweight Low-Tier UAVs (L-UAVs) function as edge servers for vehicle users, supported by a powerful High-Tier UAV (H-UAV) acting as a backup server. The objective is to minimize task execution delays while ensuring the long-term energy stability of the L-UAVs, despite unknown future system states. To this end, the problem is decoupled using Lyapunov optimization, which adaptively balances the priorities of task delays and L-UAV energy cost based on their real-time energy states. An efficient vehicle to L-UAV matching scheme is designed, and the joint optimization problem for task assignment, computing resource allocation, and trajectory control of L-UAVs and H-UAV is then solved via a Block Coordinate Descent (BCD) algorithm. Simulation results demonstrate a reduction in L-UAV transmission energy of over 26% and superior L-UAV energy stability compared to existing benchmarks.

Problem

• UAV edge computing
• energy stability
• task execution delay
• low-altitude networks
• multi-tier UAV

Innovation

• Multi-Tier UAV
• Lyapunov Optimization
• Energy Stability
• Edge Computing
• Block Coordinate Descent