Internet of Things

Wireless Security

• Physical Layer Security
• Key Generation From Wireless Channels
• Radio-Frequency Fingerprint Identification
• Physical-Layer Authentication

Wireless sensing

• Wi-Fi Sensing
• mmWave Radar Sensing

Explore Our Research →

1. 2026

   S. Gao, J. Zhang, L. Mei, S. Wang, and X. Wang, “Exploring Spatial-Temporal Representation via Star Graph for mmWave Radar-based Human Activity Recognition,” IEEE Transactions on Mobile Computing, vol. 25, no. 4, pp. 5700–5715, 2026, doi: 10.1109/tmc.2025.3634221.

   DOI arXiv BibTeX

   @article{gao2025exploring,
     title = {Exploring Spatial-Temporal Representation via Star Graph for mmWave Radar-based Human Activity Recognition},
     author = {Gao, Senhao and Zhang, Junqing and Mei, Luoyu and Wang, Shuai and Wang, Xuyu},
     journal = {IEEE Transactions on Mobile Computing},
     volume = {25},
     number = {4},
     pages = {5700--5715},
     year = {2026},
     doi = {10.1109/tmc.2025.3634221},
     arxiv = {2512.12013},
     keywords = {mmwave-radar}
   }
   

2. 2025

   Y. Guo, J. Zhang, and Y.-W. P. Hong, “Practical Physical Layer Authentication for Mobile Scenarios Using a Synthetic Dataset Enhanced Deep Learning Approach,” IEEE Transactions on Information Forensics and Security, vol. 20, pp. 9305–9317, 2025, doi: 10.1109/tifs.2025.3602265.

   DOI arXiv Data BibTeX

   @article{guo2025practical,
     title = {Practical Physical Layer Authentication for Mobile Scenarios Using a Synthetic Dataset Enhanced Deep Learning Approach},
     author = {Guo, Yijia and Zhang, Junqing and Hong, Y-W Peter},
     journal = {IEEE Transactions on Information Forensics and Security},
     volume = {20},
     pages = {9305 - 9317},
     year = {2025},
     doi = {10.1109/tifs.2025.3602265},
     arxiv = {2508.20861},
     keywords = {phy-auth, wifi, data_code}
   }
   

3. 2024

   G. Yin, J. Zhang, G. Shen, and Y. Chen, “FewSense, Towards a Scalable and Cross-Domain Wi-Fi Sensing System Using Few-Shot Learning,” IEEE Transactions on Mobile Computing, vol. 23, no. 1, pp. 453–468, 2024, doi: 10.1109/tmc.2022.3221902.

   DOI arXiv Code BibTeX

   @article{yin2022fewsense,
     title = {{FewSense}, Towards a Scalable and Cross-Domain {Wi-Fi} Sensing System Using Few-Shot Learning},
     author = {Yin, Guolin and Zhang, Junqing and Shen, Guanxiong and Chen, Yingying},
     journal = {IEEE Transactions on Mobile Computing},
     volume = {23},
     number = {1},
     pages = {453 - 468},
     year = {2024},
     doi = {10.1109/tmc.2022.3221902},
     arxiv = {2203.02014},
     keywords = {wifi-sensing, data_code}
   }
   

4. 2022

   G. Shen, J. Zhang, A. Marshall, and J. R. Cavallaro, “Towards scalable and channel-robust radio frequency fingerprint identification for LoRa,” IEEE Transactions on Information Forensics and Security, vol. 17, pp. 774–787, 2022, doi: 10.1109/tifs.2022.3152404.

   DOI arXiv Code Data BibTeX

   @article{shen2022scalable,
     title = {Towards scalable and channel-robust radio frequency fingerprint identification for {LoRa}},
     author = {Shen, Guanxiong and Zhang, Junqing and Marshall, Alan and Cavallaro, Joseph R},
     journal = {IEEE Transactions on Information Forensics and Security},
     volume = {17},
     pages = {774--787},
     year = {2022},
     doi = {10.1109/tifs.2022.3152404},
     arxiv = {2107.02867},
     keywords = {rffi,lora,data_code}
   }
   

5. 2021

   J. Zhang, R. Woods, M. Sandell, M. Valkama, A. Marshall, and J. Cavallaro, “Radio frequency fingerprint identification for narrowband systems, Modelling and classification,” IEEE Transactions on Information Forensics and Security, vol. 16, pp. 3974–3987, 2021, doi: 10.1109/tifs.2021.3088008.

   DOI BibTeX

   @article{zhang2021radio,
     title = {Radio frequency fingerprint identification for narrowband systems, Modelling and classification},
     author = {Zhang, Junqing and Woods, Roger and Sandell, Magnus and Valkama, Mikko and Marshall, Alan and Cavallaro, Joseph},
     journal = {IEEE Transactions on Information Forensics and Security},
     volume = {16},
     pages = {3974--3987},
     year = {2021},
     doi = {10.1109/tifs.2021.3088008},
     keywords = {rffi}
   }
   

All publications →

We make selected research datasets, source code and experimental resources publicly available to support reproducible research.

Explore Datasets & Code →

We are always keen to translate our research into practical applications. We have developed a range of research demonstrations to showcase our technologies and their real-world potential.

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