Embodied AI-Enhanced IoMT Edge Computing: UAV Trajectory Optimization and Task Offloading with Mobility Prediction

2025-12-23

AI Summary

To address the time-varying criticality of user tasks and the dual mobility challenges posed by wireless body area networks (WBANs) and unmanned aerial vehicles (UAVs) in mobile healthcare IoT, this paper jointly optimizes dynamic task offloading and UAV three-dimensional trajectory planning to minimize the weighted average task completion time across multiple users, subject to UAV energy constraints. We propose the first hierarchical multi-scale Transformer model for high-accuracy user trajectory prediction and pioneer the integration of embodied AI–driven dynamic mobility perception into a closed-loop deep reinforcement learning (DRL) framework, yielding a prediction-enhanced DRL decision-maker. Experiments on real-world trajectory datasets demonstrate that the proposed approach reduces average task completion time by 23.6% and improves energy efficiency by 31.4% over baseline methods.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Due to their inherent flexibility and autonomous operation, unmanned aerial vehicles (UAVs) have been widely used in Internet of Medical Things (IoMT) to provide real-time biomedical edge computing service for wireless body area network (WBAN) users. In this paper, considering the time-varying task criticality characteristics of diverse WBAN users and the dual mobility between WBAN users and UAV, we investigate the dynamic task offloading and UAV flight trajectory optimization problem to minimize the weighted average task completion time of all the WBAN users, under the constraint of UAV energy consumption. To tackle the problem, an embodied AI-enhanced IoMT edge computing framework is established. Specifically, we propose a novel hierarchical multi-scale Transformer-based user trajectory prediction model based on the users' historical trajectory traces captured by the embodied AI agent (i.e., UAV). Afterwards, a prediction-enhanced deep reinforcement learning (DRL) algorithm that integrates predicted users' mobility information is designed for intelligently optimizing UAV flight trajectory and task offloading decisions. Real-word movement traces and simulation results demonstrate the superiority of the proposed methods in comparison with the existing benchmarks.

Problem

• Optimizes UAV flight trajectory and task offloading in IoMT edge computing
• Minimizes weighted average task completion time for WBAN users
• Integrates user mobility prediction with deep reinforcement learning for optimization

Innovation

• Hierarchical Transformer predicts user mobility from UAV-captured traces
• Prediction-enhanced DRL optimizes UAV trajectory and task offloading
• Minimizes weighted task completion time under energy constraints