Authors : Dharma Trivedi; Mahek Jain; Devarsh Patel; Lakshin Pathak

Volume/Issue : Volume 10 - 2025, Issue 4 - April

---

Google Scholar : https://tinyurl.com/3wpsbd5a

DOI : https://doi.org/10.38124/ijisrt/25apr1661

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

---

Abstract : 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.

Keywords : Nano-Sensor Networks, Anomaly Detection, Deep Learning, Tinyml, Quantum Key Distribution (QKD), E91 Protocol, Medical Data Security, Smart Hospitals.

References :

1. L. Chen, H. Wang, and W. Zhang, “Nano-sensor networks for smart healthcare: Recent advances and challenges,” IEEE Transactions on NanoBioscience, vol. 21, no. 2, pp. 145–160, 2022.
2. Y. Zhang, Q. Liu, and S. Patel, “Iot-enabled smart hospitals: Architec- tures and security challenges,” in 2023 IEEE International Conference on Healthcare Informatics, pp. 1–8, IEEE, 2023.
3. X. Wang, P. Gupta, and K. Lee, “Error characterization in medical nano- sensor networks,” Nature Biomedical Engineering, vol. 7, pp. 432–445, 2023.
4. J. Li, R. Sharma, and K. Chen, “Cyber-physical threats in medical iot systems: A taxonomy and case study,” IEEE Security & Privacy, vol. 22, no. 1, pp. 56–65, 2024.
5. S. Banerjee, A. Kapoor, and T. Schmidt, “Quantum-resistant cryptog- raphy for medical iot: Requirements and solutions,” ACM Transactions on Internet of Things, vol. 6, no. 1, pp. 1–24, 2025.
6. P. Shor, “Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer,” SIAM review, vol. 41, no. 2, pp. 303–332, 1999.
7. M. Gupta, F. Liang, and D. Wong, “Deep learning for time-series analysis in medical iot: A comprehensive survey,” IEEE Access, vol. 12, pp. 12345–12380, 2024.
8. Chakraborty, S. Roy, and L. Wang, “Lightweight qkd protocols for resource-constrained medical devices,” in 2023 IEEE 19th International Conference on Body Sensor Networks, pp. 1–6, 2023.
9. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997.
10. P. Warden and D. Situnayake, TinyML: Machine Learning with Tensor- Flow Lite on Arduino and Ultra-Low-Power Microcontrollers. O’Reilly Media, 2021.
11. Ekert, “Quantum cryptography based on bell’s theorem,” Physical Review Letters, vol. 67, no. 6, p. 661, 1991.