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

TL;DR

This paper presents an embodied AI-enhanced framework for IoMT edge computing that optimizes UAV trajectories and task offloading by predicting user mobility, reducing task completion time and energy consumption.

Contribution

It introduces a hierarchical Transformer-based mobility prediction model and a prediction-enhanced DRL algorithm for UAV trajectory and task offloading optimization.

Findings

Outperforms existing benchmarks in real-world and simulated scenarios.

Effectively predicts user mobility with hierarchical Transformer models.

Achieves significant reduction in task completion time and energy use.

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.