Hierarchical Task Offloading for UAV-Assisted Vehicular Edge Computing via Deep Reinforcement Learning

2025-07-08

AI Summary

To address the challenges of heterogeneous resource coordination and poor adaptability to dynamic networks in UAV-assisted vehicular edge computing, this paper proposes a two-tier edge architecture with high-altitude and low-altitude UAV collaboration, coupled with a hierarchical offloading framework. We innovatively model the offloading ratio and UAV trajectory jointly as continuous actions, decoupling global decision-making from local scheduling, and design a priority-based local task scheduling mechanism. Leveraging the Soft Actor-Critic (SAC) algorithm, we formulate the joint optimization as a Markov Decision Process (MDP) to achieve balanced trade-offs among latency, energy consumption, and task completion rate. Experimental results demonstrate that the proposed method significantly outperforms multiple baselines in task completion rate, system efficiency, and convergence speed, while exhibiting strong robustness to the dynamics of vehicular environments.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

With the emergence of compute-intensive and delay-sensitive applications in vehicular networks, unmanned aerial vehicles (UAVs) have emerged as a promising complement for vehicular edge computing due to the high mobility and flexible deployment. However, the existing UAV-assisted offloading strategies are insufficient in coordinating heterogeneous computing resources and adapting to dynamic network conditions. Hence, this paper proposes a dual-layer UAV-assisted edge computing architecture based on partial offloading, composed of the relay capability of high-altitude UAVs and the computing support of low-altitude UAVs. The proposed architecture enables efficient integration and coordination of heterogeneous resources. A joint optimization problem is formulated to minimize the system delay and energy consumption while ensuring the task completion rate. To solve the high-dimensional decision problem, we reformulate the problem as a Markov decision process and propose a hierarchical offloading scheme based on the soft actor-critic algorithm. The method decouples global and local decisions, where the global decisions integrate offloading ratios and trajectory planning into continuous actions, while the local scheduling is handled via designing a priority-based mechanism. Simulations are conducted and demonstrate that the proposed approach outperforms several baselines in task completion rate, system efficiency, and convergence speed, showing strong robustness and applicability in dynamic vehicular environments.

Problem

• Coordinate heterogeneous computing resources in UAV-assisted vehicular networks
• Minimize system delay and energy consumption for task offloading
• Adapt to dynamic network conditions via hierarchical deep reinforcement learning

Innovation

• Dual-layer UAV architecture for edge computing
• Hierarchical offloading via soft actor-critic algorithm
• Global-local decision decoupling for optimization