Collaborative Research: NSF-MeitY: CNS Core: Small: Learning-Assisted Integrated Sensing, Communication and Security for 6G UAV Networks

List of Personnel

Principle Investigator

• Xuyu Wang, Assistant Professor

Graduate Students

• Tianya Zhao

• Jingzhou Shen

Master Students

• Luis Lago Enamorado since Spring 2025

Project Description

Unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) have emerged as a promising technology for 6G wireless networks, aiming to improve user experience and enhance people’s lives. By leveraging millimeter wave (mmWave) communications, UAV-enabled ISAC systems are expected to deliver high-throughput, ultra-reliable, and low-latency wireless communications, along with highly accurate wireless sensing and localization within 6G networks. Simultaneously, artificial intelligence (AI) and machine learning (ML) are anticipated to transform platform-based ecosystems, business models, and services in future 6G networks. The key challenge is integrating UAV localization, mmWave communications, wireless sensing, and security with AI/ML for future 6G systems. A multidisciplinary team of six investigators from Auburn University (AU), Florida International University (FIU), the Indian Institute of Technology Kanpur (IIT Kharagpur), and the International Institute of Information Technology, Naya Raipur (IIIT, Naya Raipur) collaborate closely on a project focused on learning-assisted integrated sensing, communication, and security for 6G UAV networks. The educational plan of this project includes developing joint course materials on AI/ML for UAV networks and IoT, enhancing undergraduate and graduate-level courses at the participating institutions. Simulation tools and testbeds developed through this project offer students hands-on experience with cutting-edge technology. The project outcomes are disseminated via technical publications, conference keynotes/tutorials, IEEE distinguished lectures and seminars, a project website, and open-source repositories. The investigators are committed to encouraging participation from underrepresented groups through outreach programs at their institutions and the NSF BPC/REU/RET programs throughout the project.

The project aims to develop deep learning (DL)-based localization and sensing in UAV mmWave networks, location-aided UAV mmWave communications, and joint UAV mmWave communication and radar co-design to improve mmWave spectrum utilization, wireless sensing performance, and UAV device security. The research agenda consists of five well integrated thrusts: (i) Learning-based mmWave UAV localization and wireless sensing; (ii) Joint design of location-aided UAV mmWave communications and sensing; (iii) Multiple UAV communications and sensing co-design; (iv) Learning-based RF fingerprinting for UAV security; and (v) Integration and assessment: the proposed techniques are implemented with both ray-tracing software tools and validated with extensive experiments in real, representative outdoor and indoor environments.

Related Journal and Book Chapter Publications

1. Tianya Zhao, Xuyu Wang, Shiwen Mao, Slobodan Vucetic, and Jie Wu, “Adversarial deep learning for indoor localization,” Chapter X in Network Security Empowered by Artificial Intelligence.

2. Tianya Zhao, Junqing Zhang, Shiwen Mao, and Xuyu Wang, “Explanation-guided backdoor attacks against model-agnostic RF fingerprinting systems,” IEEE Transactions on Mobile Computing, vol.24, no.3, pp.2029-2042, Mar. 2025.

3. Guolin Yin, Junqing Zhang, Xinping Yi, and Xuyu Wang. "Evasion attacks and countermeasures in deep learning-based Wi-Fi gesture recognition." IEEE Transactions on Mobile Computing (2025).

4. Guanxiong Shen, Junqing Zhang, Xuyu Wang, and Shiwen Mao. "Federated radio frequency fingerprint identification powered by unsupervised contrastive learning." IEEE Transactions on Information Forensics and Security (2024).

5. Chao Yang, Xuyu Wang, and Shiwen Mao. "TARF: Technology-agnostic RF sensing for human activity recognition." IEEE journal of biomedical and health informatics 27, no. 2 (2022): 636-647.

6. Xiangyu Wang*, Xuyu Wang*, Shiwen Mao, Jian Zhang, Senthilkumar CG Periaswamy, and Justin Patton, “Adversarial deep learning for indoor localization,” IEEE Internet of Things Journal, vol.9, no.19, pp.18182--18194, Oct. 2022. DOI: 10.1109/JIOT.2022.3155562.. (*means co-first authors)

7. Xiangyu Wang, Xuyu Wang, Shiwen Mao, Jian Zhang, Senthilkumar CG Periaswamy, and Justin Patton, “Indoor radio map construction and localization with deep Gaussian Processes,” IEEE Internet of Things Journal, vol.7, no.11, pp. 11238-11249, Nov. 2020.

Related Conference Publications

1. Tianya Zhao and Xuyu Wang, " Data-Free Backdoor Attacks on Self-Supervised Human Activity Recognition Models," in Proc. IEEE 22nd International Conference on Mobile Ad Hoc and Smart Systems (MASS 2025), Chicago, IL, Oct. 2025.

2. Jingzhou Shen and Xuyu Wang, “An Efficient and Explainable KAN Framework for Wireless Radiation Field Prediction,” in Proc. IEEE 22nd International Conference on Mobile Ad Hoc and Smart Systems (MASS 2025), Chicago, IL, Oct. 2025.

3. Ningning Wang, Yiting Wang, Tianya Zhao, Yuwei Dai, Harrison Bai, Karthik Suresh, Zhicheng Jiao, Shiwen Mao, and Xuyu Wang, " RFID-Based Vital Sign Monitoring Under Motion Using Physics-Informed Generative Models," in Proc. IEEE 22nd International Conference on Mobile Ad Hoc and Smart Systems (MASS 2025), Chicago, IL, Oct. 2025.

4. Tianya Zhao, Ningning Wang, and Xuyu Wang, "Membership Inference Against Self-supervised IMU Sensing Applications," in Proc. ACM SenSys 2025, Irvine, CA, May 2025. (Acceptance rate: 46/245=18.8%)

5. Tianya Zhao, Ningning Wang, Junqing Zhang, and Xuyu Wang, “Protocol-agnostic and Data-free Backdoor Attacks on Pre-trained Models in RF Fingerprinting,” in Proc. IEEE INFOCOM 2025, London, United Kingdom.

6. Ningning Wang, Tianya Zhao, Shiwen Mao, and Xuyu Wang, “Privacy-Preserving Wi-Fi Data Generation via Differential Privacy in Diffusion Models,” in Proc. IEEE INFOCOM 2025, London, United Kingdom.

7. Jingzhou Shen, Tianya Zhao, Yanzhao Wu, and Xuyu Wang, “NeRF-APT: A New NeRF Framework for Wireless Channel Prediction” in Proc. IEEE INFOCOM 2025 Workshop.

8. Tianya Zhao, Ningning Wang, Shiwen Mao, and Xuyu Wang, “Few-shot learning and data augmentation for cross-domain UAV fingerprinting,” in Proc. ACM MobiCom 2024 Workshop on Machine Learning for NextG Networks (MLNextG24), Washington, D.C., Nov. 2024, pp.2389-2394.

9. Tianya Zhao, Ningning Wang, Yanzhao Wu, Wenbin Zhang, and Xuyu Wang, “Backdoor Attacks Against Low-Earth Orbit Satellite Fingerprinting,” in Proc. IEEE INFOCOM 2024 workshop, Vancouver, Canada, May 2024.

10. Tianya Zhao, Xuyu Wang, Junqing Zhang, and Shiwen Mao, “Explanation-Guided Backdoor Attacks on Model-Agnostic RF Fingerprinting,” in Proc. IEEE INFOCOM 2024, Vancouver, Canada, May 2024.

11. Tianya Zhao, Xuyu Wang, and Shiwen Mao, "Cross-domain, Scalable, and Interpretable RF Device Fingerprinting,” in Proc. IEEE INFOCOM 2024, Vancouver, Canada, May 2024.

12. Tianya Zhao, Xuyu Wang, and Shiwen Mao, “Backdoor attacks against deep learning-based massive MIMO localization,” in Proc. IEEE GLOBECOM 2023, Kuala Lumpur, Malaysia, Dec. 2023.

Open Wireless Localization, Sensing and Spectrum Dataset

• 2024.10   The public wireless dataset about “Wireless Localization, Sensing, and Spectrum Dataset ” was collected.

Outreach Activities

• 2025.07   I attended an NSF Broadening Participation in Computing (BPC) workshop at Auburn University from July 1 to July 3. The workshop will involve more than 10 REU students and one wireless spectrum project.

• 2025.06   I am invited to serve as a TPC co-chair of IEEE GLOBECOM 2025 Workshop on Machine Learning and Deep Learning for Wireless Security, Taipei, Taiwan in Dec. 2025. Please consider submitting your papers.

• 2025.06  I co-orgnized a tutorial at CVPR 2025 on June 11 about "Intelligent Healthcare based on Cameras and Wireless Sensors" and gave a talk about "Wireless-based Vital Sign Monitoring and Activity Monitoring".

• 2025.06  I served as a co-chair for the IEEE ICC 2025 Workshop on Machine Learning and Deep Learning for Wireless Security in Montreal, Canada, on June 8, 2025.

• 2025.05   I am invited to serve as a symposium co-chair of AI and Machine Learning for Communications and Networking (AMCN) in ICNC 2026. Please consider submitting your papers.

• 2025,01   I am invited to serve as a Workshop Chair of GenAI4SCH: Generative AI for Smart and Connected Health: Innovations, Challenges, and Applications. Please consider submitting your papers.

• 2024.10   I am selected in the 2024 Stanford's list of World Top 2% scientists.

• 2024.10   I am invited to serve as a co-chair of IEEE INFOCOM 2025 Workshop on Deep Learning for Wireless Communications, Sensing, and Security (DeepWireless), London, United Kingdom in May 2025. Please consider submitting your papers.

Award Information

This project is supported by the National Science Foundation (NSF) under Grant CNS-2415209 from 10/01/2024 to 09/30/2027. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the foundation. [NSF link with CNS-2415209]