Outputs (45)

Explainable Adversarial Learning Framework on Physical Layer Key Generation Combating Malicious Reconfigurable Intelligent Surface (2025)
Journal Article
Wei, Z., Hu, W., Zhang, J., Guo, W., & McCann, J. (2025). Explainable Adversarial Learning Framework on Physical Layer Key Generation Combating Malicious Reconfigurable Intelligent Surface. IEEE Transactions on Wireless Communications, 24(4), 3529-3545. https://doi.org/10.1109/twc.2025.3531799

Reconfigurable intelligent surfaces (RIS) can both help and hinder the physical layer secret key generation (PL-SKG) of communications systems. Whilst a legitimate RIS can yield beneficial impacts, including increased channel randomness to enhance PL... Read More about Explainable Adversarial Learning Framework on Physical Layer Key Generation Combating Malicious Reconfigurable Intelligent Surface.

Federated Deep Reinforcement Learning-Based Intelligent Surface Configuration in 6G Secure Airport Networks (2024)
Journal Article
Chen, Y., Al-Rubaye, S., Tsourdos, A., Chu, K.-F., Wei, Z., Baker, L., & Gillingham, C. (online). Federated Deep Reinforcement Learning-Based Intelligent Surface Configuration in 6G Secure Airport Networks. IEEE Transactions on Intelligent Transportation Systems, https://doi.org/10.1109/tits.2024.3463189

Reconfigurable Intelligent Surface (RIS) is envisioned to revolutionize 6G wireless networks, particularly in complex environments like smart airports, by customizing analog beamforming with desired direction and magnitude. Through precise configurat... Read More about Federated Deep Reinforcement Learning-Based Intelligent Surface Configuration in 6G Secure Airport Networks.

Trajectory Intent Prediction of Autonomous Systems Using Dynamic Mode Decomposition (2024)
Journal Article
Perrusquía, A., Wei, Z., & Guo, W. (2024). Trajectory Intent Prediction of Autonomous Systems Using Dynamic Mode Decomposition. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54(12), 7897-7908. https://doi.org/10.1109/tsmc.2024.3462790

Proliferation of autonomous systems have increased the threat space and the economic risk in several national infrastructures, e.g., at airports. Therefore, reliable detection of their intention is paramount to ensure smooth operation of national ser... Read More about Trajectory Intent Prediction of Autonomous Systems Using Dynamic Mode Decomposition.

Classification of RF Transmitters in the Presence of Multipath Effects Using CNN-LSTM (2024)
Presentation / Conference Contribution
Patil, P., Wei, Z., Petrunin, I., & Guo, W. (2024, June). Classification of RF Transmitters in the Presence of Multipath Effects Using CNN-LSTM. Presented at 2024 IEEE International Conference on Communications Workshops (ICC Workshops), Denver, CO, USA

Radio frequency (RF) communication systems are the backbone of many intelligent transport and aerospace operations, ensuring safety, connectivity, and efficiency. Accurate classification of RF transmitters is vital to achieve safe and reliable functi... Read More about Classification of RF Transmitters in the Presence of Multipath Effects Using CNN-LSTM.

A Review of Digital Twin Technologies for Enhanced Sustainability in the Construction Industry (2024)
Journal Article
Zhang, Z., Wei, Z., Court, S., Yang, L., Wang, S., Thirunavukarasu, A., & Zhao, Y. (2024). A Review of Digital Twin Technologies for Enhanced Sustainability in the Construction Industry. Buildings, 14(4), Article 1113. https://doi.org/10.3390/buildings14041113

Carbon emissions present a pressing challenge to the traditional construction industry, urging a fundamental shift towards more sustainable practices and materials. Recent advances in sensors, data fusion techniques, and artificial intelligence have... Read More about A Review of Digital Twin Technologies for Enhanced Sustainability in the Construction Industry.

Control Layer Security: Exploiting Unobservable Cooperative States of Autonomous Systems for Secret Key Generation (2024)
Journal Article
Wei, Z., & Guo, W. (2024). Control Layer Security: Exploiting Unobservable Cooperative States of Autonomous Systems for Secret Key Generation. IEEE Transactions on Mobile Computing, 23(10), 9989-10000. https://doi.org/10.1109/tmc.2024.3369754

The rapid growth of autonomous systems (ASs) with data sharing means new cybersecurity methods have to be developed for them. Existing computational complexity-based cryptography does not have information-theoretical bounds and poses threats to super... Read More about Control Layer Security: Exploiting Unobservable Cooperative States of Autonomous Systems for Secret Key Generation.

Uncovering drone intentions using control physics informed machine learning (2024)
Journal Article
Perrusquía, A., Guo, W., Fraser, B., & Wei, Z. (2024). Uncovering drone intentions using control physics informed machine learning. Communications Engineering, 3(1), Article 36. https://doi.org/10.1038/s44172-024-00179-3

Unmanned Autonomous Vehicle (UAV) or drones are increasingly used across diverse application areas. Uncooperative drones do not announce their identity/flight plans and can pose a potential risk to critical infrastructures. Understanding drone’s inte... Read More about Uncovering drone intentions using control physics informed machine learning.

Review of Physical Layer Security in Molecular Internet of Nano-Things (2024)
Journal Article
Qiu, S., Wei, Z., Huang, Y., Abbaszadeh, M., Charmet, J., Li, B., & Guo, W. (2024). Review of Physical Layer Security in Molecular Internet of Nano-Things. IEEE Transactions on NanoBioscience, 23(1), 91-100. https://doi.org/10.1109/tnb.2023.3285973

Molecular networking has been identified as a key enabling technology for Internet-of-Nano-Things (IoNT): microscopic devices that can monitor, process information, and take action in a wide range of medical applications. As the research matures into... Read More about Review of Physical Layer Security in Molecular Internet of Nano-Things.

Securing IoT Communication Using Physical Sensor Data — Graph Layer Security with Federated Multi-agent Deep Reinforcement Learning (2023)
Presentation / Conference Contribution
Wang, L., Wei, Z., & Guo, W. (2023, July). Securing IoT Communication Using Physical Sensor Data — Graph Layer Security with Federated Multi-agent Deep Reinforcement Learning. Presented at 2023 8th International Conference on Signal and Image Processing (ICSIP), Wuxi, China

Internet-of-Things (IoT) devices are often used to transmit physical sensor data over digital wireless channels. Traditional Physical Layer Security (PLS)-based cryptography approaches rely on accurate channel estimation and information exchange for... Read More about Securing IoT Communication Using Physical Sensor Data — Graph Layer Security with Federated Multi-agent Deep Reinforcement Learning.

Control Layer Security: A New Security Paradigm for Cooperative Autonomous Systems (2023)
Journal Article
Guo, W., Wei, Z., Gonzalez, O., Perrusquía, A., & Tsourdos, A. (2024). Control Layer Security: A New Security Paradigm for Cooperative Autonomous Systems. IEEE Vehicular Technology Magazine, 19(1), 93-102. https://doi.org/10.1109/mvt.2023.3290773

Autonomous systems (ASs) often cooperate to ensure safe navigation. Embedded within the centralized or distributed coordination mechanisms are a set of observations, unobservable states, and control variables. Security of data transfer between ASs is... Read More about Control Layer Security: A New Security Paradigm for Cooperative Autonomous Systems.