Collaborative Research: CISE Core Small: NeTS: Intelligent Reflecting Surface Assisted Physical Layer Security Enhancement for Ultra-dense IoT Networks

About This Grant

The widespread adoption of the Internet of Things (IoT) forms a critical foundation for enabling applications in healthcare, transportation, and industrial automation. However, the ultra-dense deployment of IoT devices and their need to transmit sensitive data raise significant challenges for efficient and secure communication. Conventional cryptographic methods are often too computationally intensive for resource-constrained IoT devices. This project explores a lightweight and non-cryptographic framework that secures wireless communication by leveraging the randomness of physical wireless channels, grounded in information-theoretic principles of Physical Layer Security (PLS). To address challenges in dense networks where channel correlation among users is high, the project integrates Intelligent Reflecting Surfaces (IRS), passive devices capable of reconfiguring wireless signal paths, into the system design to improve both security and energy efficiency. In addition to its technical contributions, the project supports national workforce development by providing interdisciplinary research training, enhancing cybersecurity education, and engaging students across multiple institutions. This project investigates a learning-based framework to enhance Physical Layer Security (PLS) and energy efficiency in ultra-dense IoT networks using Intelligent Reflecting Surfaces (IRS). By dynamically adjusting the IRS configuration based on relational information among legitimate users and potential eavesdroppers, the system aims to increase channel disparity and mitigate eavesdropping risk. The research introduces three core innovations: (1) an IRS control strategy guided by inter-device relational states to improve secure communication channels; (2) a friendly jamming mechanism enabled by traffic pattern analysis of inactive users to further suppress adversarial interception; and (3) a secure energy efficiency optimization framework that incorporates long-term fairness across users during resource allocation. The project combines algorithm design, theoretical analysis, and real-world wireless experiments to validate system performance. Its outcomes will provide critical insights into designing adaptive, secure, and scalable communication systems for next-generation IoT environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.