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

arXiv: https://arxiv.org/abs/2512.20902 · HTML: https://mlsanity.com/paper/2512.20902/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 55 references that are in the map.
- First posted on arXiv 24 Dec 2025.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
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- [Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission](https://mlsanity.com/paper/2404.09134/) (2024)
- [Toward Edge General Intelligence with Agentic AI and Agentification: Concepts, Technologies, and Future Directions](https://mlsanity.com/paper/2508.18725/) (2025)
- [Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computing](https://mlsanity.com/paper/1911.03887/) (2019)
- [Trajectory Design and Power Control for Multi-UAV Assisted Wireless Networks: A Machine Learning Approach](https://mlsanity.com/paper/1812.07665/) (2018)
- [Artificial intelligence enabled radio propagation for communications-Part II: Scenario identification and channel modeling](https://mlsanity.com/paper/2111.12228/) (2021)
- [Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing](https://mlsanity.com/paper/1703.06060/) (2017)
- [UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimization](https://mlsanity.com/paper/1812.02658/) (2018)
- [Agentic Graph Neural Networks for Wireless Communications and Networking Towards Edge General Intelligence: A Survey](https://mlsanity.com/paper/2508.08620/) (2025)
- [Mobility in the Sky: Performance and Mobility Analysis for Cellular-Connected UAVs](https://mlsanity.com/paper/1908.07774/) (2019)
- [Optimising Energy Efficiency in UAV-Assisted Networks using Deep Reinforcement Learning](https://mlsanity.com/paper/2204.01597/) (2022)
- [Energy Efficient Computation Offloading in Aerial Edge Networks With Multi-Agent Cooperation](https://mlsanity.com/paper/2302.09021/) (2023)
- [Large Models for Aerial Edges: An Edge-Cloud Model Evolution and Communication Paradigm](https://mlsanity.com/paper/2408.04927/) (2024)
- [Deep Graph Reinforcement Learning for UAV-Enabled Multi-User Secure Communications](https://mlsanity.com/paper/2504.01446/) (2025)
- [Joint Optimization of Resource Allocation and Trajectory Control for Mobile Group Users in Fixed-Wing UAV-Enabled Wireless Network](https://mlsanity.com/paper/2209.13915/) (2022)
- [Leveraging the Power of Prediction: Predictive Service Placement for Latency-Sensitive Mobile Edge Computing](https://mlsanity.com/paper/2006.09710/) (2020)
- [Dynamic Trajectory and Offloading Control of UAV-enabled MEC under User Mobility](https://mlsanity.com/paper/2105.09042/) (2021)
- [Pre-trained Transformer Uncovers Meaningful Patterns in Human Mobility Data](https://mlsanity.com/paper/2406.04029/) (2024)

## Abstract (arXiv)

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.