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

TL;DR

This paper introduces a multi-tier UAV edge computing system that optimizes task delay and energy stability for low-altitude networks using Lyapunov optimization and joint resource management, achieving significant energy savings.

Contribution

It proposes a novel multi-tier UAV system with adaptive Lyapunov-based optimization and a vehicle-UAV matching scheme for improved energy stability and delay performance.

Findings

Over 26% reduction in L-UAV transmission energy.

Enhanced long-term energy stability of L-UAVs.

Superior performance compared to existing benchmarks.

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.