用无人机优化医疗物联网任务调度，提升实时响应速度。
Embodied AI-Enhanced IoMT Edge Computing: UAV Trajectory Optimization and Task Offloading with Mobility Prediction
- 基于无人机采集的历史轨迹，用多尺度Transformer预测用户移动。
- 结合预测信息的强化学习算法，降低所有用户的平均任务完成时间。
- 适合研究智能医疗边缘计算与无人机协同系统的学者参考。
由于具备固有的灵活性和自主运行能力，无人飞行器（UAV）已被广泛应用于互联网医疗事物（IoMT）中，为无线体域网（WBAN）用户提供实时生物医学边缘计算服务。本文针对不同WBAN用户的任务时效性动态变化特征以及WBAN用户与无人机之间的双重移动性，研究动态任务卸载与无人机飞行轨迹优化问题，旨在最小化所有WBAN用户的加权平均任务完成时间，同时满足无人机能耗约束。为此，构建了一个嵌入式AI增强的IoMT边缘计算框架。具体而言，提出一种基于嵌入式AI代理（即无人机）捕获的用户历史轨迹数据的层次化多尺度Transformer用户轨迹预测模型；随后设计一种融合用户移动性预测信息的增强型深度强化学习（DRL）算法，以智能优化无人机飞行轨迹与任务卸载决策。真实运动轨迹数据与仿真结果表明，所提方法相比现有基准具有显著优势。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。