An Intelligent RIS-Assisted Hybrid Communication-Localization Framework for Physical-Layer Authentication in Wireless Networks

As industrial internet-of-things (IIoT) networks grow, the need for reliable communication, accurate positioning, and strong security is essential. Traditional security methods, like public key infrastructure (PKI), are effective but can be vulnerable to denial-of-service (DoS) attacks due to their heavy computational and communication loads. This paper introduces a dual-mode reconfigurable intelligent surface (RIS) system that combines secure communication with physical position verification. The system uses two modes: a communication mode that uses a 64 × 64 RIS elements to improve signal strength, and a localization mode that divides the surface into four sections to create unique location fingerprints. By combining signal strength and phase responses with a simple machine learning model, we create a reliable map for verifying device locations. The proposed protocol links digital identities to these physical fingerprints, allowing the system to track moving devices securely without constant re-authentication. Experimental tests on a real hardware setup show that dividing the RIS into sections significantly improves tracking accuracy compared to standard methods. The system achieves over 99% accuracy in complex indoor settings, offering a practical and secure solution for industrial applications without needing extra expensive infrastructure.