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

TL;DR

This paper introduces a multi-tier UAV edge computing system that optimizes task processing and energy stability in low-altitude networks through adaptive resource management and trajectory planning.

Contribution

It proposes a novel multi-tier UAV system with Lyapunov optimization for energy-aware task and trajectory management, enhancing long-term energy stability.

Findings

Reduces transmission energy of L-UAVs by at least 26%.

Achieves superior energy stability compared to benchmarks.

Effectively balances task delay and energy consumption.

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.