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  • Chuyu Wang

    Chuyu Wang 王楚豫

    Nanjing University
    Computer Science

    Room 619, CS building, No.163 Xianlin Avenue, Nanjing, China, 210023

    Email: chuyu AT nju.edu.cn, wangcyu217 AT gmail.com

    Biography

    Chuyu Wang received his Ph.D. degree from the DIStributed computing LABoratory (DISLAB), Computer Science and Technology Department of Nanjing University, supervised by Prof. Sanglu Lu and Lei Xie in 2018. He joined the Computer Science and Technology Department of Nanjing University in the same year. His research interests are in the areas of wireless networking, including RFID, Mobile Computing, and Smart Sensing.

    News


  • [November 2021] Two new papers were accepted by IEEE INFOCOM 2022!

  • [November 2021] Two new papers were accepted by ACM TOSN!

  • [October 2021] Two new papers were accepted by ACM IMWUT!

  • [July 2021] Our new paper was accepted by IEEE TMC!

  • [May 2021] Our new paper was accepted by ACM IMWUT (ACM Ubicomp 2021)!

  • [November 2020] Our new paper was accepted by IEEE TMC!

  • [April 2020] Our new paper was accepted by IEEE IWQoS 2020!

  • [November 2019] Awarded "2020 ACM China Doctorial Dissertation Award Nomination" for my Ph. D. thesis "Research on Cross-Domain Sensing based on RFID Tag Array".

  • [November 2019] Awarded "2020 ACM Nanjing Chapter Doctorial Dissertation Award" for my Ph. D. thesis "Research on Cross-Domain Sensing based on RFID Tag Array".

  • [November 2019] Our new paper was accepted by IEEE INFOCOM 2020!

  • [April 2019] Our new paper was accepted by IEEE TMC!

  • [November 2018] Our one paper was accepted by IEEE INFOCOM 2019!
  • Publications

    Full publication list at [Google Scholar]
    1. Thru-the-wall Eavesdropping on Loudspeakers via RFID by Capturing Sub-mm Level Vibration.
      Abstract: The unprecedented success of speech recognition methods has stimulated the wide usage of intelligent audio systems, which provides new attack opportunities for stealing the user privacy through eavesdropping on the loudspeakers. Effective eavesdropping methods employ a high-speed camera, relying on LOS to measure object vibrations, or utilize WiFi MIMO antenna array, requiring to eavesdrop in quiet environments. In this paper, we explore the possibility of eavesdropping on the loudspeaker based on COTS RFID tags, which are prevalently deployed in many corners of our daily lives. We propose Tag-Bug that focuses on the human voice with complex frequency bands and performs the thru-the-wall eavesdropping on the loudspeaker by capturing sub-mm level vibration. Tag-Bug extracts sound characteristics through two means: (1) Vibration effect, where a tag directly vibrates caused by sounds; (2) Reflection effect, where a tag does not vibrate but senses the reflection signals from nearby vibrating objects. To amplify the influence of vibration signals, we design a new signal feature referred as Modulated Signal Difference (MSD) to reconstruct the sound from RF-signals. To improve the quality of the reconstructed sound for human voice recognition, we apply a Conditional Generative Adversarial Network (CGAN) to recover the full-frequency band from the partial-frequency band of the reconstructed sound. Extensive experiments on the USRP platform show that Tag-Bug can successfully capture the monotone sound when the loudness is larger than 60dB. Tag-Bug can efficiently recognize the numbers of human voice with 95.3%, 85.3% and 87.5% precision in the free-space eavesdropping, thru-the-brick-wall eavesdropping and thru-the-insulating-glass eavesdropping, respectively. Tag-Bug can also accurately recognize the letters with 87% precision in the free-space eavesdropping.

      Chuyu Wang, Lei Xie, Yuancan Lin, Wei Wang, Yingying Chen, Yanling Bu, Kai Zhang, and Sanglu Lu
      ACM IMWUT/UbiComp, 2022

    2. Tingjun Liu, Chuyu Wang, Lei Xie, Jingyi Ning, Tie Qiu, Fu Xiao and Sanglu Lu
      IEEE INFOCOM, 2022

    3. Lei Xie, Chuyu Wang, Yanling Bu, Jingyi Ning, and Sanglu Lu.
      Communications of the CCF, 2021
    4. Spin-Antenna: 3D Motion Tracking for Tag Array Labeled Objects via Spinning Antenna
      Abstract: Nowadays, the growing demand for the 3D human- computer interaction (HCI) has brought about a number of novel approaches, which achieve the HCI by tracking the motion of different devices, including the translation and the rotation. In this paper, we propose to use a spinning linearly polarized antenna to track the 3D motion of a specified object attached with the passive RFID tag array. Different from the fixed antenna-based solutions, which suffer from the unavoidable signal interferences at some specific positions/orientations, and only achieve the good performance in some feasible sensing conditions, our spinning antenna-based solution seeks to sufficiently suppress the ambient signal interferences and extracts the most distinctive features, by actively spinning the antenna to create the optimal sensing condition. Moreover, by leveraging the matching/mismatching property of the linearly polarized antenna, i.e., in comparison to the circularly polarized antenna, the phase variation around the matching direction is more stable, and the RSSI variation in the mismatching direction is more distinctive, we are able to find more distinctive features to estimate the position and the orientation. We build a model to investigate the RSSI and the phase variation of the RFID tag along with the spinning of the antenna, and further extend the model from a single RFID tag to an RFID tag array. Furthermore, we design corresponding solutions to extract the distinctive RSSI and phase values from the RF-signal variation. Our solution tracks the translation of the tag array based on the phase features, and the rotation of the tag array based on the RSSI variation. The experimental results show that our system can achieve an average error of 13.6cm in the translation tracking, and an average error of 8.3◦ in the rotation tracking in the 3D space.

      Chuyu Wang, Lei Xie, Keyan Zhang, Wei Wang, Yanling Bu, and Sanglu Lu
      IEEE INFOCOM, 2019
    5. Probing into the Physical Layer: Moving Tag Detection for Large-Scale RFID Systems.
      Abstract: Logistics monitoring is a fundamental application that utilizes RFID systems to manage numerous tagged-objects. Due to the frequent rearrangement of tagged-objects, a fast RFID-based tracking approach is highly desired for accurate logistics distribution. However, traditional RFID systems usually take tens of seconds to interrogate hundreds of RFID tags, not to mention the time delay involved to locate all the tags, which severely prevents from in-time tracking. To address this issue, we reduce the problem domain by first distinguishing the motion status of the tagged-objects, i.e., "stationary" or "moving", and then tracking the moving objects with the state-of-the-art localization schemes, which significantly reduces the efforts of tracking all the objects. Toward this end, we propose a moving tag detection mechanism, which achieves the time efficiency by exploiting the useless collision signal in RFID systems. In particular, we extract two kinds of physical-layer features (namely phase profile and backscatter link frequency) from the collision signal received by the USRP to distinguish tags at different positions. We further develop the Graph Matching (GM) method and Coherent Phase Variance (CPV) method to detect the moving tagged-objects. Experiment results show that our approach can accurately detect the moving objects while reducing 80% inventory time compared with the state-of-art solutions.

      Chuyu Wang, Lei Xie, Wei Wang, Yingying Chen, Tao Xue, and Sanglu Lu
      IEEE Transactions on Mobile Computing
    6. RF-ECG: Heart Rate Variability Assessment based on COTS RFID Tag Array
      Abstract: As an important indicator of autonomic regulation for circulatory function, Heart Rate Variability (HRV) is widely used for general health evaluation. Apart from using dedicated devices (e.g, ECG) in a wired manner, current methods search for a ubiquitous manner by either using wearable devices, which suffer from low accuracy and limited battery life, or applying wireless techniques (e.g., FMCW), which usually utilize dedicated devices (e.g., USRP) for the measurement. To address these issues, we present RF-ECG based on Commercial-Off-The-Shelf (COTS) RFID, a wireless approach to sense the human heartbeat through an RFID tag array attached on the chest area in the clothes. In particular, as the RFID reader continuously interrogates the tag array, two main effects are captured by the tag array: the reflection effect representing the RF-signal reflected from the heart movement due to heartbeat; the moving effect representing the tag movement caused by chest movement due to respiration. To extract the reflection signal from the noisy RF-signals, we develop a mechanism to capture the RF-signal variation of the tag array caused by the moving effect, aiming to eliminate the signals related to respiration. To estimate the HRV from the reflection signal, we propose a signal reflection model to depict the relationship between the RF-signal variation from the tag array and the reflection effect associated with the heartbeat. A fusing technique is developed to combine multiple reflection signals from the tag array for accurate estimation of HRV. Experiments with 15 volunteers show that RF-ECG can achieve a median error of 3% of Inter-Beat Interval (IBI), which is comparable to existing wired techniques.

      Chuyu Wang, Lei Xie, Wei Wang, Yingying Chen, Yanling Bu, Sanglu Lu
      ACM IMWUT/UbiComp, 2018
    7. RF-Kinect: A Wearable RFID-based Approach Towards 3D Body Movement Tracking
      Abstract: The rising popularity of electronic devices with gesture recognition capabilities makes the gesture-based human-computer interaction more attractive. Along this direction, tracking the body movement in 3D space is desirable to further facilitate behavior recognition in various scenarios. Existing solutions attempt to track the body movement based on computer version or wearable sensors, but they are either dependent on the light or incurring high energy consumption. This paper presents RF-Kinect, a training-free system which tracks the body movement in 3D space by analyzing the phase information of wearable RFID tags attached on the limb. Instead of locating each tag independently in 3D space to recover the body postures, RF-Kinect treats each limb as a whole, and estimates the corresponding orientations through extracting two types of phase features, Phase Difference between Tags (PDT) on the same part of a limb and Phase Difference between Antennas (PDA) of the same tag. It then reconstructs the body posture based on the determined orientation of limbs grounded on the human body geometric model, and exploits Kalman filter to smooth the body movement results, which is the temporal sequence of the body postures. The real experiments with 5 volunteers show that RF-Kinect achieves 8.7◦ angle error for determining the orientation of limbs and 4.4cm relative position error for the position estimation of joints compared with Kinect 2.0 testbed.

      Chuyu Wang, Jian Liu, Yingying Chen, Lei Xie, Hongbo Liu, Sanglu Lu.
      ACM IMWUT/UbiComp, 2018
    8. Multi-Touch in the Air: Concurrent Micromovement Recognition Using RF Signals
      Abstract: The human–computer interactions have moved from the conventional approaches of entering inputs into the keyboards/touchpads to the brand-new approaches of performing interactions in the air. In this paper, we propose RF-glove, a sys- tem that recognizes concurrent multiple finger micromovement using RF signals, so as to realize the vision of "multi-touch in the air." It uses a commercial-off-the-shelf (COTS) RFID reader with three antennas and five COTS tags attached to the five fingers of a glove, one tag per finger. During the process of a user performing finger micromovements, we let the RFID reader continuously interrogate these tags and obtain the backscattered RF signals from each tag. For each antenna–tag pair, the reader obtains a sequence of RF phase values called a phase profile from the tag’s responses over time. To tradeoff between accuracy and robustness in terms of matching resolution, we propose a two phase approach, including coarse-grained filtering and fine- grained matching. To tackle the variation of template phase profiles at different positions, we propose a phase-model-based solution to reconstruct the template phase profiles based on the exact locations. Experiment results show that we achieve an average accuracy of 92.1% under various moving speeds, orientation deviations, and so on.

      Lei Xie, Chuyu Wang, Alex. X. Liu, Jianqiang Sun, and Sanglu Lu.
      ACM/IEEE Transactions on Networking, vol. 26, no. 1, pp. 231-244, 2018

    Selected Awards

  • [2019] ACM China Doctorial Dissertation Award Nomination 2020

  • [2019] ACM Nanjing Chapter Doctorial Dissertation Award 2020

  • [2018] Best-in-Session Presentation Award in IEEE INFOCOM 2018

  • [2017] Program A for outstanding PhD candidate of Nanjing University

  • [2016] National Scholarship for doctoral students

  • [2015] First Prize in the Second National College Competition on Internet of Things

  • [2015] Joint-PhD Student Scholarship of China Scholarship Council

  • [2012] Excellence plan at Nanjing University