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CRII: CNS: RUI: Exploiting Robust Deep Learning Framework for Wireless Localization Systems in Adversarial IoT Environments
List of Personnel
Principle Investigator
Graduate Students
Undergraduate Students
Alumni
Project Description
With the proliferation of wireless networks and mobile devices, wireless Internet of Things (IoT) applications (e.g., location-based services) have gained considerable attention. Indoor localization faces a number of challenges in the radio propagation environment, including the multipath effect, shadowing, fading, and delay distortion. To tackle the non-line-of-sight (NLOS) indoor environment, fingerprinting based wireless localization methods using deep neural networks (DNN) have been proposed. However, a data-driven only approach using DNN may perform poorly in adversarial IoT environments (e.g., wireless jamming). Specifically, DNN models are shown to be vulnerable to adversarial examples generated by introducing a subtle perturbation. Thus, the primary aim of the proposed research is to develop robust solutions for wireless localization in adversarial IoT environments, which fills in the gap between wireless localization accuracy and robustness. Particularly, we consider adversarial machine learning for wireless localization in IoT environments. The successful completion of this project will significantly improve the state-of-the-art of wireless localization and enable robust IoT applications. The project's educational plan includes developing a new graduate-level course on deep learning for wireless IoT systems and enhancing various core undergraduate and graduate-level courses. Also, the project strives to broaden participation from under-represented groups in research and will continue to greatly strengthen such efforts throughout the project years.
The project research agenda is composed of two closely integrated research thrusts. In Thrust I, this project will use adversarial deep learning for indoor localization in a way that leverages adversarial training in the offline stage to improve the robustness of the deep network, thus alleviating the threat of the adversarial example attacks on wireless data. This project will consider two wireless localization tasks: adversarial examples for wireless localization in black-box attacks and unsupervised learning for adversarial examples detection. In Thrust II, this project will combine deep learning and Gaussian processes for uncertain location estimation, to improve robustness for wireless localization algorithms. Specifically, this project will exploit uncertainty location estimation with deep Gaussian process against both white-box and black-box attacks. Also, this project will model and analyze the fundamental limits and robustness of wireless localization. For all the proposed tasks in the two thrusts, this project will develop mathematical models and solution algorithms. The proposed algorithms will be implemented with wireless IoT devices/platforms (e.g., Wi-Fi, RFID, and LoRa), and validated with extensive experiments in representative indoor environments.
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.
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.
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)
Chao Yang, Xuyu Wang, and Shiwen Mao, “RFID tag localization with a sparse tag array,” IEEE Internet of Things Journal, Special Issue on Knowledge and Service Oriented Industrial Internet of Things: Architectures, Challenges and Methodologies, vol.9, no.18, pp.16976-16989, Sept. 2022. DOI: 10.1109/JIOT.2021.3137723.
Chao Yang, Shiwen Mao, Xuyu Wang, “An overview of 3GPP positioning standards,” ACM GetMobile, vol.26, no.1, pp.9-13, Mar. 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)
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.
Xiangyu Wang, Xuyu Wang, and Shiwen Mao, “Indoor fingerprinting with bimodal CSI tensors: A deep residual sharing learning approach,” IEEE Internet of Things Journal, vol.8, no.6, pp.4498-4513, Mar. 2021.
Related Conference Publications
Tianya Zhao, Ningning Wang, Guanqun Cao, Shiwen Mao, and Xuyu Wang, “Functional Data Analysis Assisted Cross-Domain Wi-Fi Sensing Using Few-Shot Learning,,” in Proc. IEEE ICC 2024, Denver, CO, June 2024.
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.
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, “Backdoor attacks against deep learning-based massive MIMO localization,” in Proc. IEEE GLOBECOM 2023, Kuala Lumpur, Malaysia, Dec. 2023.
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.
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.
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.
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.
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.
Harshit Ambalkar, Xuyu Wang, and Shiwen Mao, “Adversarial human activity recognition using Wi-Fi CSI,” invited paper, in Proc. 2021 Annual IEEE Canadian Conference of Electrical and Computer Engineering (CCECE'21), Virtual Conference, Sept. 2021.
Xuyu Wang, Mohini Patil, Chao Yang, Shiwen Mao, and Palak Anilkumar Patel, “Deep Convolutional Gaussian Processes for mmWave outdoor localization,” in Proc. IEEE ICASSP 2021, Special Session on Contactless and Wireless Sensing for Smart Environments, Toronto, Canada, June 2021.
Jait Purohit, Xuyu Wang, Shiwen Mao, Xiaoyan Sun, and Chao Yang, “Fingerprinting-based indoor and outdoor localization with LoRa and deep learning,” in Proc. IEEE GLOBECOM 2020, Taipei, Taiwan, Dec. 2020.
Chao Yang, Xuyu Wang, and Shiwen Mao, “SparseTag: High-precision backscatter indoor localization with sparse RFID tag arrays,” in Proc. IEEE SECON 2019, Boston, MA, June 2019.
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 Localization, Sensing and 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 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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2023.09 I am invited to serve as a TPC co-chair of IEEE ICC 2024 Workshop on Machine Learning and Deep Learning for Wireless Security, Denver, Colorado USA in June 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-2321763/2105416 from 07/01/2021 to 06/30/2024. 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-2321763] [NSF link with CNS-2105416]
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