FUSION: Forecast-Embedded Agent Scheduling with Service Incentive Optimization over Distributed Air-Ground Edge Networks
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911357424631808 |
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| author | Qi, Houyi Liwang, Minghui Hosseinalipour, Seyyedali Fu, Liqun Zou, Sai Wang, Xianbin Ni, Wei Hong, Yiguang |
| author_facet | Qi, Houyi Liwang, Minghui Hosseinalipour, Seyyedali Fu, Liqun Zou, Sai Wang, Xianbin Ni, Wei Hong, Yiguang |
| contents | In this paper, we introduce a first-of-its-kind forecasting-driven, incentive-inherent service provisioning framework for distributed air-ground integrated networks that explicitly accounts for human-machine coexistence. In our framework, vehicular-UAV agent pairs (APs) are proactively dispatched to overloaded hotspots to augment the computing capacity of edge servers (ESs), which in turn gives rise to a set of challenges that we jointly address: 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 (HUs) and machine users (MUs). To address these challenges, we propose FUSION, a two-stage optimization framework, consisting of an offline stage and an online stage. In the offline stage, a liquid neural network-powered module performs multi-step spatio-temporal demand forecasting at distributed ESs, whose outputs are exploited by an enhanced ant colony optimization-based routing scheme and an auction-based incentive-compatible contracting mechanism, to jointly determine ES-AP contracts and pre-planned service routes. In the online stage, we formulate the congestion-aware task scheduling as a potential game among HUs, MUs, and heterogeneous ES/UAVs, and devise a potential-guided best-response dynamics algorithm that provably converges to a pure-strategy Nash equilibrium. Experiments on both synthetic and real-world datasets show that FUSION consistently achieves higher social welfare and improved resource utilization, while maintaining latency and energy costs comparable to state-of-the-art baselines and preserving individual rationality, budget balance, and near-truthfulness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_14323 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FUSION: Forecast-Embedded Agent Scheduling with Service Incentive Optimization over Distributed Air-Ground Edge Networks Qi, Houyi Liwang, Minghui Hosseinalipour, Seyyedali Fu, Liqun Zou, Sai Wang, Xianbin Ni, Wei Hong, Yiguang Networking and Internet Architecture In this paper, we introduce a first-of-its-kind forecasting-driven, incentive-inherent service provisioning framework for distributed air-ground integrated networks that explicitly accounts for human-machine coexistence. In our framework, vehicular-UAV agent pairs (APs) are proactively dispatched to overloaded hotspots to augment the computing capacity of edge servers (ESs), which in turn gives rise to a set of challenges that we jointly address: 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 (HUs) and machine users (MUs). To address these challenges, we propose FUSION, a two-stage optimization framework, consisting of an offline stage and an online stage. In the offline stage, a liquid neural network-powered module performs multi-step spatio-temporal demand forecasting at distributed ESs, whose outputs are exploited by an enhanced ant colony optimization-based routing scheme and an auction-based incentive-compatible contracting mechanism, to jointly determine ES-AP contracts and pre-planned service routes. In the online stage, we formulate the congestion-aware task scheduling as a potential game among HUs, MUs, and heterogeneous ES/UAVs, and devise a potential-guided best-response dynamics algorithm that provably converges to a pure-strategy Nash equilibrium. Experiments on both synthetic and real-world datasets show that FUSION consistently achieves higher social welfare and improved resource utilization, while maintaining latency and energy costs comparable to state-of-the-art baselines and preserving individual rationality, budget balance, and near-truthfulness. |
| title | FUSION: Forecast-Embedded Agent Scheduling with Service Incentive Optimization over Distributed Air-Ground Edge Networks |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2512.14323 |