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

Yufei Ye, Shijian Gao, Xinhu Zheng, Liuqing Yang

TL;DR

This work tackles long-term energy stability in a low-altitude, multi-tier UAV edge computing setup by introducing LATUS, where lightweight L-UAVs handle edge tasks and an H-UAV provides a mobile backup. An online Lyapunov optimization framework converts the stochastic problem into per-slot deterministic tasks, combining a vehicle-to-L-UAV matching stage with a block coordinate descent optimization of task offloading, computing resources, and UAV trajectories. The approach yields over 26% reduction in L-UAV transmission energy and improved energy stability compared with benchmarks, while maintaining competitive task delays. The results demonstrate the method’s effectiveness in dynamically balancing delay and energy under uncertain workloads, supporting energy-efficient, resilient IoV edge computing deployments.

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.

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

TL;DR

This work tackles long-term energy stability in a low-altitude, multi-tier UAV edge computing setup by introducing LATUS, where lightweight L-UAVs handle edge tasks and an H-UAV provides a mobile backup. An online Lyapunov optimization framework converts the stochastic problem into per-slot deterministic tasks, combining a vehicle-to-L-UAV matching stage with a block coordinate descent optimization of task offloading, computing resources, and UAV trajectories. The approach yields over 26% reduction in L-UAV transmission energy and improved energy stability compared with benchmarks, while maintaining competitive task delays. The results demonstrate the method’s effectiveness in dynamically balancing delay and energy under uncertain workloads, supporting energy-efficient, resilient IoV edge computing deployments.

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.
Paper Structure (22 sections, 43 equations, 9 figures, 2 tables, 2 algorithms)

This paper contains 22 sections, 43 equations, 9 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Network Model
  4. UAV Motion Model
  5. Communication Model
  6. Computation Model
  7. Problem Formulation
  8. Problem Description
  9. Problem Transformation
  10. Joint Task Assignment, Resource Allocation, and UAV Trajectory Optimization
  11. Vehicle to L-UAV Matching Algorithm
  12. Joint Optimization of Task Assignment Ratio and UAV Computing Resource Allocation
  13. Trajectory Optimization for L-UAVs and H-UAV
  14. Complexity Analysis
  15. Performance Analysis
  16. ...and 7 more sections

Figures (9)

  • Figure 1: Illustration of multi-tier UAV edge computing architecture in air-ground networks.
  • Figure 2: The two-stage matching scheme between vehicles and L-UAVs.
  • Figure 3: The comparison of average task execution delay among different methods with varying numbers of vehicles.
  • Figure 4: The comparison of average L-UAV transmission energy among different methods.
  • Figure 5: The comparison of the delay to energy deviation ratio among different methods.
  • ...and 4 more figures