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AG-MPBS: a Mobility-Aware Prediction and Behavior-Based Scheduling Framework for Air-Ground Unmanned Systems

Tianhao Shao, Kaixing Zhao, Feng Liu, Lixin Yang, Bin Guo

TL;DR

This work tackles the challenge of dynamically recruiting autonomous UAVs and UGVs for time-sensitive tasks by introducing MPBS, a framework that combines behavior-based mobility classification with time-varying Markov mobility prediction and a priority-driven scheduling mechanism. By treating devices as recruitable users and leveraging base-station metrics, MPBS proactively matches tasks to suitable devices while respecting spatiotemporal constraints. Experimental results on the GeoLife dataset demonstrate improved task completion rates, reduced response times, and higher resource utilization compared to baselines. The framework offers a scalable, predictive solution for intelligent, collaborative scheduling in air-ground unmanned systems with real-world applicability.

Abstract

As unmanned systems such as Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) become increasingly important to applications like urban sensing and emergency response, efficiently recruiting these autonomous devices to perform time-sensitive tasks has become a critical challenge. This paper presents MPBS (Mobility-aware Prediction and Behavior-based Scheduling), a scalable task recruitment framework that treats each device as a recruitable "user". MPBS integrates three key modules: a behavior-aware KNN classifier, a time-varying Markov prediction model for forecasting device mobility, and a dynamic priority scheduling mechanism that considers task urgency and base station performance. By combining behavioral classification with spatiotemporal prediction, MPBS adaptively assigns tasks to the most suitable devices in real time. Experimental evaluations on the real-world GeoLife dataset show that MPBS significantly improves task completion efficiency and resource utilization. The proposed framework offers a predictive, behavior-aware solution for intelligent and collaborative scheduling in unmanned systems.

AG-MPBS: a Mobility-Aware Prediction and Behavior-Based Scheduling Framework for Air-Ground Unmanned Systems

TL;DR

This work tackles the challenge of dynamically recruiting autonomous UAVs and UGVs for time-sensitive tasks by introducing MPBS, a framework that combines behavior-based mobility classification with time-varying Markov mobility prediction and a priority-driven scheduling mechanism. By treating devices as recruitable users and leveraging base-station metrics, MPBS proactively matches tasks to suitable devices while respecting spatiotemporal constraints. Experimental results on the GeoLife dataset demonstrate improved task completion rates, reduced response times, and higher resource utilization compared to baselines. The framework offers a scalable, predictive solution for intelligent, collaborative scheduling in air-ground unmanned systems with real-world applicability.

Abstract

As unmanned systems such as Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) become increasingly important to applications like urban sensing and emergency response, efficiently recruiting these autonomous devices to perform time-sensitive tasks has become a critical challenge. This paper presents MPBS (Mobility-aware Prediction and Behavior-based Scheduling), a scalable task recruitment framework that treats each device as a recruitable "user". MPBS integrates three key modules: a behavior-aware KNN classifier, a time-varying Markov prediction model for forecasting device mobility, and a dynamic priority scheduling mechanism that considers task urgency and base station performance. By combining behavioral classification with spatiotemporal prediction, MPBS adaptively assigns tasks to the most suitable devices in real time. Experimental evaluations on the real-world GeoLife dataset show that MPBS significantly improves task completion efficiency and resource utilization. The proposed framework offers a predictive, behavior-aware solution for intelligent and collaborative scheduling in unmanned systems.

Paper Structure

This paper contains 18 sections, 9 equations, 4 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Mobile Crowdsensing
  4. Air-Ground Heterogeneous Collaboration
  5. Recruitment and Location Prediction
  6. System Model and Problem Formulation
  7. System Model
  8. Problem Formulation
  9. Recruitment Framework and Scheduling Algorithm
  10. Device Behavior Classification using KNN
  11. Time-Varying Markov Prediction Model
  12. Recruitment Expectation Estimation
  13. Priority-Based Scheduling
  14. Experiments and Evaluation
  15. Dataset and Simulation Configuration
  16. ...and 3 more sections

Figures (4)

  • Figure 1: Overall workflow of the proposed MPBS framework.
  • Figure 2: Employee position transfer frequency.
  • Figure 3: Performance Comparison of Scheduling Algorithms: MPBS achieves superior TCR, lower ART, and higher DU compared to baseline methods.
  • Figure 4: Comparison of base-station-centric MPBS scheduling vs EDF and LSF strategies under varying base station/task ratios.