FUSION: Forecast-Embedded Agent Scheduling with Service Incentive Optimization over Distributed Air-Ground Edge Networks

2025-12-16

AI Summary

This work addresses core challenges in human–machine coexistence scenarios within air–ground integrated edge networks: high-dynamic spatiotemporal load uncertainty, tight coupling between aerial and terrestrial resources, prediction-driven contract risk, and heterogeneous QoS requirements of human users (HuUs) and machine users (MaUs). We propose a prediction-embedded two-stage framework: (i) an offline stage integrating Pro-LNN-based multi-step spatiotemporal forecasting, eACO-VRP-enhanced path planning, and an off-line auction-based incentive-compatible contract mechanism (Off-AIC²); and (ii) an online stage formulated as a potential game, solved via a provably convergent PG-BR algorithm—potential-guided best-response. Evaluated on both synthetic and real-world trajectory datasets, our framework significantly improves social welfare, end-to-end latency, resource utilization, and system robustness, consistently outperforming state-of-the-art baselines.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Multiagent Systems: Mechanism Design

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: Applied ML and AI for Web-based mobile applications

Abstract

We investigate a forecasting-driven, incentive-compatible service provisioning framework for distributed air-ground integrated edge networks under human-machine coexistence. We consider hybrid players where the computing capacity of edge servers (ESs) are augmented by vehicular-UAV agent pairs (AgPs) that can be proactively dispatched to overloaded hotspots. Unique key challenges should be addressed: highly uncertain spatio-temporal workloads, spatio-temporal coupling between road traffic and UAV capacity, forecast-driven contracting risks, and heterogeneous quality-of-service (QoS) requirements of human users (HuUs) and machine users (MaUs). To cope with these issues, we propose FUSION, a two-stage framework that tightly couples service demand prediction, agent deployment, and task scheduling. In the offline stage, a Pro-LNN module performs intelligent multi-step spatio-temporal demand forecasting at distributed ESs, whose outputs are exploited by an enhanced ant colony optimization-based routing scheme (eACO-VRP) and an auction-based incentive-compatible contracting mechanism (Off-AIC^2), to jointly determine ES-AgP contracts and pre-planned service routes. In the online stage, we formulate congestion-aware task scheduling as an potential game among HuUs, MaUs, and heterogeneous ES/UAV providers, and devise a potential-guided best-response dynamics (PG-BR) algorithm that provably converges to a pure-strategy Nash equilibrium. Extensive experiments on both synthetic and real-world traces show that FUSION significantly improves social welfare, latency, resource utilization, and robustness compared with state-of-the-art baselines.

Problem

• Forecasting-driven service provisioning in distributed air-ground edge networks
• Incentive-compatible scheduling with spatio-temporal workload uncertainties
• Optimizing heterogeneous QoS for human and machine users

Innovation

• Two-stage framework integrates demand forecasting and agent deployment
• Offline stage uses enhanced ant colony optimization for routing
• Online stage employs potential game for congestion-aware scheduling