collaborators

11 papers

cs.NI2026

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

Houyi Qi, Minghui Liwang, Seyyedali Hosseinalipour +5

In this paper, we introduce a first-of-its-kind forecasting-driven, incentive-aware service provisioning framework for distributed air-ground integrated networks that explicitly ac…

cs.NI2026

STEPS: Semantic Contract-Guided Scheduling for LLM-Assisted Natural Language-Driven Edge AI Services

Houyi Qi, Minghui Liwang, Xianbin Wang +2

Edge user/service scheduling has become a cornerstone of distributed AI systems, determining where and how AI services are executed under limited communication and computing resour…

cs.NI2026

Masking Intent, Sustaining Equilibrium: Risk-Aware Potential-Game-Based Service Provision in Dynamic Mobile Crowdsensing

Houyi Qi, Minghui Liwang, Kaiwen Tan +5

Mobile crowdsensing (MCS) is evolving from basic data collection to dynamic service provisioning, where platforms must maintain task completion, budget feasibility, and sensing qua…

cs.NI2026

Zero-Trust Bilateral Edge Service Trading with Deposit-Refund Regulation for Runtime Compliance

Houyi Qi, Minghui Liwang, Zhipeng Cheng +1

Privacy-sensitive edge services necessitate optimizing diverse-type resource scheduling to support trustworthy provisioning within a zero-trust security framework. However, existin…

cs.NI2026

Forecasting-Driven Stable Successor Matching for UAV-Assisted Continuous Edge Services

Houyi Qi, Minghui Liwang, Yuhan Su +1

Continuous and reliable service support is crucial for emerging latency-sensitive and computation-intensive applications in UAV-assisted edge networks (UENs) due to operational dyn…

cs.NI2026

Risk-Budgeted Online Scheduling for Continuous Edge Inference over Evolving Time Horizons

Houyi Qi, Minghui Liwang, Sai Zou +1

Continuous edge inference necessitates not merely low per-timeslot latency, but sustained timeliness guarantees in the presence of time-varying channels, fluctuating edge workloads…