Cloud-Edge-End Collaboration for 6G Medical Internet of Things

Existing Internet of Medical Things (IoMT) systems often struggle with the high latency and energy constraints required for real-time physio-logical monitoring. To address these challenges, this paper proposes a “Terminal-Edge-Cloud” collaborative architecture specifically designed for 6G-enabled medical applications. Leveraging 6G’s Ultra-Reliable Low-Latency Communication (URLLC) and Terahertz bandwidth, we devise a hierarchical task offloading strategy: terminal nodes perform lightweight signal preprocessing to conserve energy; edge nodes execute time-critical tasks such as fatigue detection using pre-trained models; and the cloud handles computation-intensive deep learning training and multi-sensor fusion. Theoretical analysis and architectural comparison demonstrate that this dynamic collaboration significantly reduces system latency compared to cloud-only models and improves resource utilization over static edge paradigms. The proposed framework offers a scalable solution for next-generation intelligent healthcare, ensuring both the timeliness of emergency alerts and the accuracy of long-term health analysis.