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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月16日

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  • Umar Hayat Khan,
  • Affaq Qamar,
  • Rahim Khan,
  • Fahad Alturise,
  • Abdul Rahman Alshaabani,
  • Salem Alkhalaf

摘要

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.