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Collaborative Research: CNS Core: Medium: Data Augmentation and Adaptive Learning for Next Generation Wireless Spectrum Systems
List of Personnel
Principle Investigator
Graduate Students
Tianya Zhao
Ningning Wang
Undergraduate Students
Alumni
Project Description
Deep learning has shown great promise in solving many open challenges in wireless networking research and applications. Deep learning is data hungry, and one of the critical obstacles towards fulfilling its promise is facilitating the acquisition of sufficient amounts of data to train and validate deep learning models. The primary goal of this project is to devise innovative approaches that enable wireless researchers and practitioners to acquire data more efficiently at reduced cost and to utilize existing data more effectively. Findings from this project are expected to fuel future breakthroughs in wireless research by making deep learning models more widely applicable. By integrating research and education, the proposed work will provide excellent hands-on exercises, research, and educational opportunities for undergraduate and graduate students at the three collaborating universities. The project will leverage the existing diversity-related outreach programs at the three institutions to broaden participation from under-represented groups.
A team of four investigators with complementary expertise from Auburn University, Temple University, and Florida International University will carry out a coherent research agenda consisting of the following four thrusts: (1) Spectrum data synthesis and augmentation aided by generative adversarial networks; (2) Exploiting historical and synthetic wireless networking data through novel transfer learning algorithms; (3) Characterizing the relationship between dataset size and performance; (4) Integrate, validate and apply approaches developed in the first three thrusts on spectrum database construction, RF spectrum anomaly detection, and transmitter classification. Thrusts 1-3 are application-agnostic and focused on studying fundamental concepts and techniques that facilitate the acquisition of sufficient amounts of wireless data, enable more effective utilization of existing data, and enable the prediction of how much data is needed to meet desired performance. Thrust 4 is application-specific and focused on specific wireless applications where deep learning has been applied and demonstrated great potential. The data, software and education materials developed from this project will be widely disseminated. The project will engage industry stakeholders on project-related issues, with the aim to disseminate ideas and learn relevant challenges faced by the industry when applying deep learning to wireless applications.
Related Journal and Book Chapter Publications
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.
Jing Hou, Xuyu Wang, and Amy Z Zeng, “Inter-Temporal Reward Strategies in the Presence of Strategic Ethical Hackers,” in IEEE/ACM Transactions on Networking, 2024.
Azhar Chara, Tianya Zhao, Xuyu Wang, and Shiwen Mao, “Respiratory biofeedback using acoustic sensing with smartphones,” Elsevier Smart Health Journal, vol. 28, pp.100387, June 2023.
Chao Yang, Xuyu Wang, and Shiwen Mao, “TARF: Technology-agnostic RF sensing for human activity recognition,” IEEE Journal of Biomedical and Health Informatics, Special Issue on Cognitive Cyber-Physical Systems with AI based Solutions in Medical Informatics, vol.27, no.2, pp.636--647, Feb. 2023.
Chao Yang, Xuyu Wang, and Shiwen Mao, “RFID based 3D human pose tracking: A subject generalization approach,” Elsevier/KeAi Digital Communications and Networks, Special Issue on Edge computation and intelligence, vol.8, no.3, pp.278-288, Aug. 2022.
Chao Yang, Lingxiao Wang, Xuyu Wang, and Shiwen Mao, “Environment adaptive RFID based 3D human pose tracking with a meta-learning approach,” IEEE Journal of Radio Frequency Identification, Special Issue on Wireless Motion Capture and Fine-Scale Localization, vol.6, no.1, pp.413-425, Jan. 2022.
Chao Yang, Xuyu Wang, Shiwen Mao, “Respiration monitoring with RFID in driving environments,” IEEE Journal on Selected Areas in Communications, Special Issue on Internet of Things for In-Home Health Monitoring, vol.39, no.2, pp.500-512, Feb. 2021. (2022 Best Journal Paper Award of IEEE ComSoc eHealth Technical Committee)
Chao Yang, Xuyu Wang, and Shiwen Mao, “Unsupervised drowsy driving detection with RFID,” IEEE Transactions on Vehicular Technology, vol.69, no.8, pp. 8151-8163, Aug. 2020.
Chao Yang, Xuyu Wang, and Shiwen Mao, “Unsupervised detection of apnea using commodity RFID tags with a recurrent variational autoencoder,” IEEE Access Journal, Special Section on Advanced Information Sensing and Learning Technologies for Data-centric Smart Health Applications, vol.7, no.1, pp.67526-67538, June 2019.
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
Ningning Wang, Tianya Zhao, Shiwen Mao, and Xuyu Wang, "AI Generated Wireless Data for Enhanced Satellite Device Fingerprinting” in Proc. IEEE ICC 2024 workshop, Denver, CO, June 2024.
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.
Tianya Zhao, Xuyu Wang, and Shiwen Mao, "Cross-domain, Scalable, and Interpretable RF Device Fingerprinting,” in Proc. IEEE INFOCOM 2024, Vancouver, Canada, May 2024.
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.
Nawrin Tabassum, Ka-Ho Chow, Xuyu Wang, Wenbin Zhang, and Yanzhao Wu, "On the Efficiency of Privacy Attacks in Federated Learning," in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024) Workshops - FedVision.
Hongpeng Jin, Wenqi Wei, Xuyu Wang, Wenbin Zhang, Yanzhao Wu, “Rethinking Learning Rate Tuning in the Era of Large Language Models,” will appear in IEEE CogMI 2023 Vision Track.
Jing Hou, Xuyu Wang, and Amy Z. Zeng, “Inter-Temporal Reward Decisions with Strategic Ethical Hackers,” in Proc. IEEE CNS 2023, Orlando, FL, Oct. 2023.
Zichong Wang, Nripsuta Saxena, Tongjia Yu, Sneha Karki, Tyler Zetty, Israat Haque, Shan Zhou, Dukka Kc, Ian Stockwell, Xuyu Wang, Albert Bifet and Wenbin Zhang, “Preventing Discriminatory Decision-making in Evolving Data Stream,” in Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT), Chicago, USA, 2023. (ACM FAcct 2023 Best Paper Award).
Harshit Ambalkar, Tianya Zhao, Xuyu Wang, and Shiwen Mao, “Adversarial attack and defense for WiFi-based apnea detection system,” in Proc. IEEE INFOCOM Posters, Hoboken, NJ, May 2023.
Steven Mackey, Tianya Zhao, Xuyu Wang, and Shiwen Mao, “Poster Abstract: Cross-Domain Adaptation for RF Fingerprinting Using Prototypical Networks,” in Proc. ACM SenSys 2022, Boston, MA, Nov. 2022.
Shivenkumar Parmar, Xuyu Wang, Chao Yang, and Shiwen Mao, “Voice fingerprinting for indoor localization with a single microphone array and deep learning,” in Proc. the Fourth ACM Wireless Security and Machine Learning Workshop (WiseML'22), in conjunction with ACM WiSec 2022, San Antonio, TX, May 2022.
Ushasree Boora, Xuyu Wang, and Shiwen Mao, “Robust massive MIMO localization using neural ODE in adversarial environments,” in Proc. IEEE ICC 2022, Seoul, South Korea, May 2022.
Erbo Shen, Weidong Yang, Xuyu Wang, Shiwen Mao, and Wei Bin, “TagSense: Robust wheat moisture and temperature sensing using a passive RFID tag,” in Proc. IEEE ICC 2022, Seoul, South Korea, May 2022. (The IEEE ICC 2022 Best Paper Award).
Chao Yang, Lingxiao Wang, Xuyu Wang, and Shiwen Mao, “Demo Abstract: Environment-adaptive 3D human pose tracking with RFID,” in Proc. IEEE INFOCOM 2022, Virtual Conference, May 2022. (The IEEE INFOCOM 2022 Demo Award).
Chao Yang, Xuyu Wang, and Shiwen Mao, “Demo Abstract: Technology-agnostic approach to RF based human activity recognition,” in Proc. IEEE INFOCOM 2022, Virtual Conference, May 2022.
Mansi Patel, Xuyu Wang, and Shiwen Mao, “Data augmentation with Conditional GAN for automatic modulation classification,” in Proc. 2020 ACM Workshop on Wireless Security and Machine Learning (WiseML 2020), in conjunction with the 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks (ACM WiSec 2020), Linz, Austria, July 2020, pp.31-36.
- Xuyu Wang, and Shiwen Mao, “Meta-Pose: Environment-adaptive human skeleton tracking with RFID,” in Proc. IEEE GLOBECOM 2021, Madrid, Spain, Dec. 2021.
Chao Yang, Xuyu Wang, and Shiwen Mao, “Subject-adaptive skeleton tracking with RFID,” in Proc. The 16th IEEE International Conference on Mobility, Sensing and Networking (MSN 2020), Tokyo, Japan, Dec. 2020, pp.599-606.
Chao Yang, Xuyu Wang, and Shiwen Mao, “RFID-based driving fatigue detection,” in Proc. IEEE GLOBECOM 2019, Waikoloa, HI, Dec. 2019. (The IEEE GLOBECOM 2019 Best Paper Award)
Chao Yang, Xuyu Wang, and Shiwen Mao, “AutoTag: Recurrent variational autoencoder for unsupervised apnea detection with RFID tags,” in Proc. IEEE GLOBECOM 2018, Abu Dhabi, United Arab Emirates, Dec. 2018, pp.1-7.
Xiangyu Wang*, Xuyu Wang*, Shiwen Mao, Jian Zhang, Senthilkumar C.G. Periaswamy, and Justin Patton, “DeepMap: Deep Gaussian Process for indoor radio map construction and location estimation,” in Proc. IEEE GLOBECOM 2018, Abu Dhabi, United Arab Emirates, Dec. 2018. (*means Co-first authors)
Open Wireless Spectrum Dataset
Outreach Activities
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2024.06 I am invited to serve as a TPC co-chair of IEEE GLOBECOM 2024 Workshop on Machine Learning and Deep Learning for Wireless Security, Cape Town, South Africa in Dec. 2024. Please consider submitting your papers.
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2024.06 I am invited to serve as a TPC co-chair of IEEE GLOBECOM 2024 Workshop on Machine Learning and Deep Learning for Wireless Security, Cape Town, South Africa in Dec. 2024. Please consider submitting your papers.
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2024.06 I served as a co-chair for the IEEE ICC 2024 Workshop on Machine Learning and Deep Learning for Wireless Security in Denver, Colorado, USA on June 9, 2024.
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2023.10 I am invited to serve as a co-chair of IEEE INFOCOM 2024 Workshop on Deep Learning for Wireless Communications, Sensing, and Security (DeepWireless), Vancouver, Canada in May 2024. Please consider submitting your papers.
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2022.11 I am invited to serve as a co-chair of IEEE INFOCOM 2023 Workshop on Deep Learning for Wireless Communications, Sensing, and Security (DeepWireless), New York Area, May 2023. Please consider submitting your papers.
Award Information
This project is supported by the National Science Foundation (NSF) under Grant CNS-2107164 from 10/01/2021 to 09/30/2024. 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-2317190] [NSF link with CNS-2107164 ]
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