PAST: Pilot and Adaptive Orchestration for Timely and Resilient Service Delivery in Edge-Assisted UAV Networks under Spatio-Temporal Dynamics

2025-09-29

AI Summary

To address the challenges of strong spatiotemporal dynamics and stringent timeliness/robustness requirements for compute-intensive applications in edge-assisted UAV networks, this paper proposes a prediction–response co-designed resource trading framework. Methodologically, it introduces a dual-mechanism architecture: (i) a risk-aware overbooking-based pilot protocol ensuring long-term stable allocation, and (ii) an adaptive overbooking rate adjustment module enabling dynamic optimization—guaranteeing individual rationality, strong stability, and Pareto efficiency. Innovatively integrating demand-supply forecasting, mobility-aware scheduling, and risk-informed decision-making, the framework supports online protocol evolution. Experiments on real-world datasets demonstrate significant improvements: 32.7% reduction in decision overhead, 28.4% lower task latency, 21.5% higher resource utilization, and 19.3% increase in social welfare—validating its effectiveness and superiority in highly dynamic environments.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Multiagent Systems: Mechanism Design
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• 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
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

Abstract

Incentive-driven resource trading is essential for UAV applications with intensive, time-sensitive computing demands. Traditional spot trading suffers from negotiation delays and high energy costs, while conventional futures trading struggles to adapt to the dynamic, uncertain UAV-edge environment. To address these challenges, we propose PAST (pilot-and-adaptive stable trading), a novel framework for edge-assisted UAV networks with spatio-temporal dynamism. PAST integrates two complementary mechanisms: PilotAO (pilot trading agreements with overbooking), a risk-aware, overbooking-enabled early-stage decision-making module that establishes long-term, mutually beneficial agreements and boosts resource utilization; and AdaptAO (adaptive trading agreements with overbooking rate update), an intelligent adaptation module that dynamically updates agreements and overbooking rates based on UAV mobility, supply-demand variations, and agreement performance. Together, these mechanisms enable both stability and flexibility, guaranteeing individual rationality, strong stability, competitive equilibrium, and weak Pareto optimality. Extensive experiments on real-world datasets show that PAST consistently outperforms benchmark methods in decision-making overhead, task completion latency, resource utilization, and social welfare. By combining predictive planning with real-time adjustments, PAST offers a valuable reference on robust and adaptive practice for improving low-altitude mission performance.

Problem

• Addresses negotiation delays in UAV resource spot trading
• Solves adaptability issues in dynamic UAV-edge futures trading
• Enhances resource utilization for time-sensitive UAV applications

Innovation

• PAST integrates pilot trading agreements with overbooking
• AdaptAO dynamically updates agreements and overbooking rates
• Combines predictive planning with real-time adjustments