Secure edge-based IoMT framework for ICU monitoring with TinyML and post-quantum cryptography

Abstract

This article proposes a secure, real-time, intelligent, edge-based Internet of Medical Things (IoMT) monitoring framework for intensive care unit (ICU) environments. The system integrates TinyML-powered decision trees with lattice-based post-quantum cryptography (PQC), specifically Kyber 512, to enable low-latency anomaly detection and quantum-resistant data transmission. Although designed for deployment on resource-constrained ESP32 microcontrollers, the entire pipeline is implemented and evaluated within an OMNeT++ simulation environment, including on-device inference and PQC encryption. A synthetically generated ICU dataset, validated by three hospitals, ensures clinical relevance and robustness in diverse patient scenarios. Data fusion techniques improve feature reliability, while Kyber 512 provides lightweight, quantum-safe encryption. OMNeT++ simulations demonstrate end-to-end communication with zero observed packet loss and very low end-to-end latency, under realistic ICU network conditions. The framework addresses key challenges in computational efficiency, data confidentiality, and scalability, offering a future-ready solution for intelligent healthcare systems. The results show 99.4% accuracy in anomaly detection, with strong generalization validated on external datasets (PhysioNet: 98.5%, Kaggle: 99.0%). This work represents one of the first integrations of TinyML and PQC in a simulated IoMT setting, paving the way for secure, scalable, and intelligent ICU monitoring.