Lightweight PUF-Based Hardware Fingerprinting for Secure IoT Sensor Interfaces
Keywords:
Physical Unclonable Functions,, Hardware Security, Internet of Things, Sensor Interfaces, Lightweight CryptographyAbstract
The rapid proliferation of Internet of Things devices has introduced unprecedented conveniences in industrial, medical, and consumer applications. However, the ubiquitous deployment of these resource-constrained devices has simultaneously expanded the attack surface, particularly at the physical sensor interface. Traditional cryptographic primitives often exceed the computational and power budgets of edge sensor nodes, necessitating alternative security paradigms. This paper proposes a lightweight, hardware-based fingerprinting mechanism leveraging Physical Unclonable Functions to secure Internet of Things sensor interfaces. By exploiting inherent manufacturing variations in standard complementary metal-oxide-semiconductor processes, Physical Unclonable Functions generate unique, unclonable digital signatures that serve as dynamic hardware fingerprints. We introduce a novel Ring Oscillator based Physical Unclonable Function architecture optimized for minimal area overhead and high reliability under varying environmental conditions. The proposed system integrates seamlessly into standard sensor communication buses, providing robust authentication and data integrity verification without the need for secure non-volatile memory key storage. Comprehensive experimental evaluations conducted on Field Programmable Gate Array platforms demonstrate that our design achieves near-ideal uniqueness and exceptional reliability across a wide range of temperatures and supply voltages. Furthermore, the lightweight nature of the fingerprinting protocol significantly reduces power consumption and latency compared to conventional cryptographic approaches, presenting a highly scalable solution for next-generation secure Internet of Things ecosystems.
References
1. Liao, Qiuyue, et al. "Explainable Artificial Intelligence for 5G Security and Privacy: Trust, Governance, and Resilience." (2025).
2. Tao, J., Lyu, R., & Cao, X. (2026). A Scalable Data Governance Architecture for Privacy-Aware Intelligent Learning Systems in Lifelong. Future-Adaptive Intelligence and Lifelong Systems, 1 (1).
3. Zhang, Luyan. "MCP: A Control-Theoretic Orchestration Framework for Synergistic Efficiency and Interpretability in Multimodal Large Language Models." arXiv preprint arXiv:2509.16597 (2025).
4. Qu, D., Ma, Y., Yan, J., & Pyrozhenko, M. (2026). TriMeta-BFNet: A Tri-Meta Stacked Atypical-Frequency Bayesian Fourier Neural Network for Hallucination-Resistant Community Detection. Mathematics, 14 (13), 2283.
5. Zhang, Y., Zhang, Y., Zhao, M., Feng, S., & Cheung, Y. M. (2026). How to Ask the AI: A User Perspective Survey for Large Language Model Prompting.IEEE Transactions on Artificial Intelligence.
6. Yao, Y., Shu, F., Li, Z., Cheng, X., & Wu, L. (2023). Secure transmission scheme based on joint radar and communication in mobile vehicular networks. IEEE transactions on intelligent transportation systems, 24 (9), 10027-10037.
7. Long, J., Xu, Z., Jiang, T., Yao, W., Jia, S., Ma, C., & Chen, X. (2025, April). Robust SAM: on the adversarial robustness of vision foundation models. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 6, pp. 5775-5783).
8. Ma, Y., & Qu, D. (2026, February). IMFM-GNN: Interpretable Multimodel GNN Via Fourier-Markov Dynamics for Community Detection in Sustainable Energy Networks. In 2026 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) (pp. 1-6). IEEE.
9. Hu, X., Wan, Z., & Murthy, N. N. (2019). Dynamic pricing of limited inventories with product returns. Manufacturing & Service Operations Management, 21(3), 501-518.
10. Liu, Z., Qian, S., Cao, S., & Shi, T. (2025). Mitigating age-related bias in large language models: Strategies for responsible artificial intelligence development. INFORMS Journal on Computing.
11. Zhang, Q., Gao, W., Liu, C., Yao, Y., Yan, S., Shu, F., & Jin, S. (2025). Covert transmission for active RIS-aided full-duplex UAV integrated sensing, communication, and computation systems. IEEE Journal on Selected Areas in Communications.
12. Wuyang Zhang and Shichao Pei. 2026. Your LLM Agent Can Leak Your Data: Data Exfiltration via Backdoored Tool Use. arXiv:2604.05432 [cs.CR] doi:10.48550/arXiv.2604.05432
13. Yang, Z., Ji, W., & Wang, Z. (2024). Adaptive joint configuration optimization for collaborative inference in edge-cloud systems. Science China Information Sciences, 67(4), 149103.
14. Qu, D., Ma, Y., & Wang, Y. (2026). STR-GNN: stability-regularized graph neural networks for suppressing spurious temporal variations in dynamic community detection. Scientific Reports.
15. Yang, Z., Liang, B., & Ji, W. (2021). An intelligent end–edge–cloud architecture for visual IoT-assisted healthcare systems. IEEE internet of things journal, 8(23), 16779-16786.
16. Wang, S., Zhang, X., Wang, P., & Li, X. (2026). Design of Magnetic Sensor Array-Based PUFs for IoT Security. IEEE Transactions on Instrumentation and Measurement, 75, 1-9.
17. Ma, Y., & Qu, D. (2026). Machine Learning Based Markov Decision Framework for Optimizing Circular Economy Systems.Engineering Proceedings,120(1), 44.
18. Tao, J., Lyu, R., & Cao, X. (2026). A Deep Learning-Based Automated Content Moderation Framework for Online Platforms.Future-Adaptive Intelligence and Lifelong Systems,1(1).
19. Sun, Fang, et al. "Modeling Structural Deviation in 10-K Risk Factors: A Semantic Anomaly Detection and Explainable AI Approach." Risks 14.4 (2026): 87.
20. Yang, Z., Ji, W., Guo, Q., & Wang, Z. (2023, October). Javp: Joint-aware video processing with edge-cloud collaboration for dnn inference. In Proceedings of the 31st ACM International Conference on Multimedia (pp. 9152-9160).
21. Ma, Y., Qu, D., & Wang, Y. (2026). HALO-GNN: hallucination-resistant temporal graph neural networks for dynamic community detection.Scientific Reports.
22. Zhao, Q., & Huang, Y. (2026). DAREA: A Dynamic AI-Driven Architecture for Real Estate Asset Tokenization on Blockchain.Fundamental Scientific Reports in Multidisciplinary Areas,2(02), 182-192.
23. Li, X., Wang, P., Li, G., Ni, L., & Zhang, Y. (2023). Design of interface circuits and lightweight PUF for TMR sensors. IEEE Sensors Journal, 23(11), 11754-11761.
24. Li, X., Wang, P., Li, G., & Zhang, Y. (2023). Design of a novel self-test-on-chip interface ASIC for capacitive accelerometers. IEEE Transactions on Circuits and Systems I: Regular Papers, 70(7), 2834-2843.
25. Wang Y, Fang Y, Wang T, et al. Dreamnav: A trajectory-based imaginative framework for zero-shot vision-and-language navigation[J]. arXiv preprint arXiv:2509.11197, 2025.
26. Issa, M. A., Chen, H., Wang, J., & Imani, M. (2024). CyberRL: Brain-inspired reinforcement learning for efficient network intrusion detection.IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems,44(1), 241-250.
27. Yao, Y., Zhao, J., Li, Z., Cheng, X., & Wu, L. (2023). Jamming and eavesdropping defense scheme based on deep reinforcement learning in autonomous vehicle networks. IEEE Transactions on Information Forensics and Security, 18, 1211-1224.
28. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Why should I trust you?: Explaining the predictions of any classifier. In Proceedings of KDD.
29. Li, X., Yin, L., Chen, W., Gao, Z., & Liu, X. (2017). A high-resolution tunneling magneto-resistance sensor interface circuit.Modern Physics Letters B,31(04), 1750030.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Software Engineering and Artificial Intelligence Systems

This work is licensed under a Creative Commons Attribution 4.0 International License.