Quantum-Assisted Secure Nano-Network Traffic Framework for Real-Time Medical Data Transmission in Smart Hospitals

The integration of nano-sensor networks in smart hospitals enables high-resolution real-time monitoring of critical patient health metrics. However, the transmission of medical data over nano-networks poses significant challenges related to data security and anomaly detection. In this paper, we propose a deep learning (DL)-based anomaly classification framework integrated with quantum-assisted E91 protocol for secure key exchange. The framework classifies nano-traffic as normal or anomalous using lightweight models like TinyML, LSTM, and GRU, optimized via various optimizers. The E91 quantum key distribution (QKD) ensures secure and tamper-resistant transmission of classified medical data across hospital networks.