AI-Driven Resource Allocation in Edge Metaverse for the Internet of Medical Things (IoMT)

Abstract

Healthcare delivery is increasingly dependent on the Internet of Medical Things (IoMT), yet the rapid growth of heterogeneous devices and dynamic network conditions make resource allocation, latency control, and privacy preservation critical challenges. In modern healthcare, ensuring timely and accurate activity recognition is essential for improving patient care and enabling real-time clinical decision-making. The variability in device capacity and communication links restricts the effectiveness of conventional resource management strategies. This research presents a predictive, privacy-preserving framework for intelligent resource optimization in IoMT environments, leveraging Federated Reinforcement Learning (FRL) with a Deep Q-Network (DQN) to support distributed decision-making without exposing sensitive health data. The study integrates a Hybrid FRL-ConvLSTM model for activity recognition, enabling accurate workload prediction to guide scheduling and offloading strategies between device and edge layers. Simulation experiments under diverse workloads and network conditions confirm that the proposed approach achieves significant improvements in reducing delay, and bandwidth efficiency. The proposed model is effective for real-time healthcare monitoring and demonstrates that combining predictive deep learning with FRL-based optimization can significantly enhance reliability, and privacy in IoMT systems, it gives accuracy of 99.5% .