Wei Wang    chinese


Nanjing University

Professor

State Key Laboratory for Novel Software and Technology
Department of Computer Science and Technology
Nanjing University

Room 606, CS building, No.163 Xianlin Avenue, Nanjing, China, 210023

Office Hours: Every Thursday 8:00 - 12:00

ww AT nju.edu.cn



WeiWang

News

Biography

Wei Wang received his BS and MS degree from the ESE Department of Nanjing University in 1997 and 2000, respectively. He received Ph.D. degree from the ECE department of National University of Singapore (NUS) in 2008. He joined the Computer Science and Technology Department of Nanjing University in 2012. Before that, he has worked at Microsoft Research Asia (MSRA) as an associate researcher in 2009. His research interests are in the areas of wireless networking, including Device-free Sensing, Software Defined Radio, and Mobile Cellular Networks.

Research Interests

Selected Publications

Full publication list at [Google Scholar] and [DBLP]

Conferences

  1. DEWS: A Distributed Measurement Scheme for Efficient Wireless Sensing
    Abstract: One of the key challenges for wireless sensing systems is how to efficiently enable wireless sensing capabilities for multiple devices while leveraging existing wireless communication resources. In this paper, we propose DEWS, a distributed channel measurement scheme that allows multiple transmitters to perform sensing tasks simultaneously, which considers three key issues in wireless sensing tasks: multi-device sensing resolution, multi-device sensing reliability, and multi-device sensing accuracy. First, we use a carefully designed distributed Resource Unit (dRU) allocation scheme based on OFDMA to ensure that multiple devices perform sensing tasks simultaneously with the entire bandwidth, thereby improving the sensing resolution. Then, a subcarrier linear phase shift scheme is designed to avoid signal interference between different transmitting devices and improve the reliability of multi-device sensing. This scheme realizes the fine-grained Time Division Multiplexing (TDM) of multiple devices, which can also increase the number of simultaneous access of sensing devices. Finally, a sensing accuracy improvement algorithm combining a single antenna-based motion recovery method utilizing Independent Component Analysis (ICA) and sampling frequency offset (SFO) correction is proposed, to further help DEWS to break the inherent bandwidth limits and recover different motions at similar distances. We verify the effectiveness of DEWS through extensive experimental testing using commercial USRPs.

    Mingzhi Pang, Kaiying Li, Xun Wang, Wei Wang, Wen Cheng, Dongxu Liu, Yuqing Yin, Pengpneg Chen
    ACM IMWUT/UbiComp, 2025 (to appear)
    UbiComp '25
  2. Security Attacks on LLM-based Code Completion Tools
    Abstract: The rapid development of large language models (LLMs) has significantly advanced code completion capabilities, giving rise to a new generation of LLM-based Code Completion Tools (LCCTs). Unlike general-purpose LLMs, these tools possess unique workflows, integrating multiple information sources as input and prioritizing code suggestions over natural language interaction, which introduces distinct security challenges. Additionally, LCCTs often rely on proprietary code datasets for training, raising concerns about the potential exposure of sensitive data. This paper exploits these distinct characteristics of LCCTs to develop targeted attack methodologies on two critical security risks: jailbreaking and training data extraction attacks. Our experimental results expose significant vulnerabilities within LCCTs, including a 99.4% success rate in jailbreaking attacks on GitHub Copilot and a 46.3% success rate on Amazon Q. Furthermore, We successfully extracted sensitive user data from GitHub Copilot, including 54 real email addresses and 314 physical addresses associated with GitHub usernames. Our study also demonstrates that these code-based attack methods are effective against general-purpose LLMs, such as the GPT series, highlighting a broader security misalignment in the handling of code by modern LLMs. These findings underscore critical security challenges associated with LCCTs and suggest essential directions for strengthening their security frameworks. The code and attack samples from our research are provided in the appendix. Disclaimer. This paper contains examples of harmful language. Reader discretion is recommended.

    Wen Cheng, Ke Sun, Xinyu Zhang, Wei Wang
    AAAI, 2025 (to appear)
    AAAI '25
  3. MINA: Fine-grained In-network Aggregation Resource Scheduling for Machine Learning Service
    Abstract: In-network aggregation (INA) offloads gradient aggregation onto switches, and thus effectively reduces the aggregation latency and the volume of traffic. However, INA resources are limited due to the high cost of on-chip memory, which imposes distinct challenges to the effective scheduling of these resources in multi-job MLaaS scenarios. In this paper, we explore the scheduling of INA resources in spatial and temporal dimensions, specifically focusing on its impact on the average job completion time (JCT) and the efficiency of INA resources. We propose MINA, an innovative co-design of algorithm and system that intelligently assigns INA resources to each job and effectively schedules these resources among multiple jobs. Our experiments show that MINA attains an INA efficiency score of 0.9998, implying that almost all jobs run nearly as efficiently as they would with exclusive INA acceleration.

    Shichen Dong, Zhixiong Niu, Mingchao Zhang, Zhiying Xu, Chuntao Hu, Pengzhi Zhu, Qingchun Song, Lei Qu, Peng Cheng, Can-Tu Nguyen, Shaoling Sun, Xiaohu Xu, Yongqiang Xiong, Wei Wang, Xiaoliang Wang
    IEEE INFOCOM, 2025 (to appear)
    INFOCOM '25
  4. USee: Ultrasound-based Device-free Eye Movement Sensing
    Abstract: Detecting eye movements has become a hot topic in human-computer interaction and serves as an overall health indicator, making it both appealing and challenging. In this paper, we present a system named USee that achieves high-precision capture of weak and aperiodic eye movements by utilizing fine-grained and ubiquitous ultrasound signals, capturing both blinking and more subtle saccades. We initially identify signal changes associated with eye movements by leveraging the distinctive impact of blinking on signals. Pioneeringly, we reveal the relationship between the residuals of signal decomposition and subtle eye movements. Utilizing innovative signal processing architectures, we mitigate interference and extract eye movement features. Subsequently, we employ one-dimensional convolutional operations in place of signal cross-correlation, designing filters for motion category identification and a lightweight convolutional neural network for saccade direction classification. This capability allows our system to serve as a foundational sensing layer for eye movements, making it applicable to various applications. We implement USee on both a research-purpose platform and a commodity Raspberry Pi. Extensive experimental results validate the effectiveness of our system, demonstrating a saccade recognition accuracy of 91% and a blinking recognition accuracy of 94%. The system exhibits robustness, persisting even in challenging scenarios with strong interference, such as the presence of a moving pedestrian.

    Wen Cheng, Mingzhi Pang, Haoran Wan, Shichen Dong, Dongxu Liu, Wei Wang
    IEEE SECON, 2024 Best Paper Award
    SECON '24
  5. mP-Gait: Fine-grained Parkinson’s Disease Gait Impairment Assessment with Robust Feature Analysis
    Abstract: Patients with Parkinson’s disease (PD) often show gait impairments including shuffling gait, festination, and lack of arm and leg coordination. Quantitative gait analysis can provide valuable insights for PD diagnosis and monitoring. Prior work has utilized 3D motion capture, foot pressure sensors, IMUs, etc. to assess the severity of gait impairment in PD patients These sensors, despite their high precision, are often expensive and cumbersome to wear which makes them not the best option for long-term monitoring and naturalistic deployment settings. In this paper, we introduce mP-Gait, a millimeter-wave (mmWave) radar-based system designed to detect the gait features in PD patients and predict the severity of their gait impairment. Leveraging the high frequency and wide bandwidth of mmWave radar signals, mP-Gait is able to capture high-resolution reflected signals from different body parts during walking. We develop a pipeline to detect walking, extract gait features using signal analysis methods, and predict patients’ UPDRS-III gait scores with a machine learning model. As gait features from PD patients with gait impairment are correctly and robustly extracted, mP-Gait is able to observe the fine-grained gait impairment severity fluctuation caused by medication response. To evaluate mP-Gait, we collected gait features from 144 participants (with UPDRS-III gait scores between 0 and 2) containing over 4000 gait cycles. Our results show that mP-Gait can achieve a mean absolute error of 0.379 points in predicting UPDRS-III gait scores.

    Wenhao Zhang, Haipeng Dai, Dongyu Xia, Yang Pan, Zeshui Li, Wei Wang, Zhen Li, Lei Wang, Guihai Chen
    ACM IMWUT/UbiComp, 2024
    UbiComp '24
  6. CSS: Built-in Channel State Scrambling for Secure Wi-Fi based Sensing
    Abstract: This paper proposes CSS, a built-in channel state scrambling scheme to provide always-on protection for channel state information. CSS uses randomly generated scrambling vectors to emulate human activities, preventing eavesdroppers from recovering human physical activity from the channel state. By carefully designing the scrambling scheme based on physical channel models, we ensure that legacy receivers can successfully decode the frames and all frames transmitted over the air are scrambled. Furthermore, legitimate Wi-Fi sensors can still recover the true activity with a pre-shared secret key. Implementation on a real communication system shows that CSS can retain the same frame-error rate for commercial wireless receivers while misleading eavesdroppers with a success rate of over 95%.

    Xiao Deng, Dongyu Xia, Xun Wang, Shuyu Shi, Wei Wang
    IEEE ICDCS, 2024
    ICDCS '24
  7. ALT: Breaking the Wall between Data Layout and Loop Optimizations for Deep Learning Compilation
    Abstract: Deep learning models rely on highly optimized tensor libraries for efficient inference on heterogeneous hardware. Current deep compilers typically predetermine layouts of tensors and then optimize loops of operators. However, such unidirectional and one-off workflow strictly separates graph-level optimization and operator-level optimization into different system layers, missing opportunities for unified tuning. This paper proposes ALT, a deep compiler that performs joint graph-level layout optimization and operator-level loop optimization. ALT provides a generic transformation module to manipulate layouts and loops with easy-to-use primitive functions. ALT further integrates an auto-tuning module that jointly optimizes graph-level data layouts and operator-level loops while guaranteeing efficiency. Experimental results show that ALT significantly outperforms state-of-the-art compilers (e.g., Ansor) in terms of both single operator performance (e.g., 1.5x speedup on average) and end-to-end inference performance (e.g., 1.4x speedup on average).

    Zhiying Xu, Jiafan Xu, Hongding Peng, Wei Wang, Xiaoliang Wang, Haoran Wan, Haipeng Dai, Yixu Xu, Hao Cheng, Kun Wang, Guihai Chen
    EuroSys, 2023
    EuroSys '23
  8. AGO: Boosting Mobile AI Inference Performance by Removing Constraints on Graph Optimization
    Abstract: Abstract—Traditional deep learning compilers rely on heuristics for subgraph generation, which impose extra constraints on graph optimization, e.g., each subgraph can only contain at most one complex operator. In this paper, we propose AGO, a framework for graph optimization with arbitrary structures to boost the inference performance of deep models by removing such constraints. To create new optimization opportunities for complicated subgraphs, we propose intensive operator fusion, which effectively stitches multiple complex operators together for better performance. Further, we design a graph partitioning scheme that allows an arbitrary structure for each subgraph while guaranteeing the acyclic property among all generated subgraphs. Additionally, to enable efficient performance tuning for complicated subgraphs, we devise a divide-and-conquer tuning mechanism to orchestrate different system components. Through extensive experiments on various neural networks and mobile devices, we show that our system can improve the inference performance by up to 3.3x when compared with state-of-the-art vendor libraries and deep compilers.

    Zhiying Xu, Hongding Peng, Wei Wang
    IEEE INFOCOM, 2023
    INFOCOM '23
  9. Sequence-based Device-free Gesture Recognition Framework for Multi-channel Acoustic Signals
    Abstract: Device-free gesture recognition schemes based on acoustic sensing signals are promising solutions for next-generation human-computer interaction systems. However, existing gesture recognition frameworks reuse visual neural networks toperform feature extraction. These approaches ignore the time sequence nature of the acoustic signal and treat acoustic echoprofiles solely as 2D images. In this paper, we propose a time-sequence-based deep learning framework that can exploit thespatio-temporal information of sensing signals. The framework first fuses multi-channel acoustic signals to extract spatial gesture features from a single acoustic frame and then uses the Transformer network to discover the timing relationsbetween spatial features. Our extensive evaluations with real-world datasets show that our light-weighted framework outperforms the state-of-the-art in classifying 14 gestures andachieves an average accuracy of 95.85%.

    Zhizheng Yang, Xun Wang, Dongyu Xia, Wei Wang, Haipeng Dai
    IEEE ICASSP, 2023
    ICASSP '23
  10. mSilent: Towards General Corpus Silent Speech Recognition Using COTS mmWave Radar
    Abstract: Silent speech recognition (SSR) allows users to speak to the device without making a sound, avoiding being overheard or disturbing others. Compared to the video-based approach, wireless signal-based SSR can work when the user is wearing a mask and has fewer privacy concerns. However, previous wireless-based systems are still far from well-studied, e.g. they are only evaluated in corpus with highly limited size, making them only feasible for interaction with dozens of deterministic commands. In this paper, we present mSilent, a millimeter-wave (mmWave) based SSR system that can work in the general corpus containing thousands of daily conversation sentences. With the strong recognition capability, mSilent not only supports the more complex interaction with assistants, but also enables more general applications in daily life such as communication and input. To extract fine-grained articulatory features, we build a signal processing pipeline that uses a clustering-selection algorithm to separate articulatory gestures and generates a multi-scale detrended spectrogram (MSDS). To handle the complexity of the general corpus, we design an end-to-end deep neural network that consists of a multi-branch convolutional front-end and a Transformer-based sequence-to-sequence back-end. We collect a general corpus dataset of 1,000 daily conversation sentences that contains 21K samples of bi-modality data (mmWave and video). Our evaluation shows that mSilent achieves a 9.5% average word error rate (WER) at a distance of 1.5m, which is comparable to the performance of the state-of-the-art video-based approach. We also explore deploying mSilent in two typical scenarios of text entry and in-car assistant, and the less than 6% average WER demonstrates the potential of mSilent in general daily applications.

    Shang Zeng, Haoran Wan, Shuyu Shi, Wei Wang
    ACM IMWUT/UbiComp, 2023
    UbiComp '23
  11. MagSound: Magnetic Field Assisted Wireless Earphone Tracking
    Abstract: Wireless earphones are pervasive acoustic sensing platforms that can be used for many applications such as motion tracking and handwriting input. However, wireless earphones suffer clock offset between the connected smart devices, which would accumulate error rapidly over time. Moreover, compared with smartphone and voice assistants, the acoustic signal transmitted by wireless earphone is much weaker due to the poor frequency response. In this paper, we propose MagSound, which uses the built-in magnets to improve the tracking and acoustic sensing performance of Commercial-Off-The-Shelf (COTS) earphones. Leveraging magnetic field strength, MagSound can predict the position of wireless earphones free from clock offset, which can be used to re-calibrate the acoustic tracking. Further, the fusion of the two modalities mitigates the accumulated clock offset and multipath effect. Besides, to increase the robustness to noise, MagSound employs finely designed Orthogonal Frequency-Division Multiplexing (OFDM) ranging signals. We implement a prototype of MagSound on COTS and perform experiments for tracking and handwriting input. Results demonstrate that MagSound maintains millimeter-level error in 2D tracking, and improves the handwriting recognition accuracy by 49.81%. We believe that MagSound can contribute to practical applications of wireless earphones-based sensing.

    Lihao Wang, Wei Wang, Haipeng Dai, Shizhe Liu
    ACM IMWUT/UbiComp, 2023
    UbiComp '23
  12. VECTOR: Velocity Based Temperature-field Monitoring with Distributed Acoustic Devices
    Abstract: Ambient temperature distribution monitoring is desired in a variety of real-life applications including indoors temperature control and building energy management. Traditional temperature sensors have their limitations in the aspects of single point/item based measurements, slow response time and huge cost for distribution estimation. In this paper, we introduce VECTOR, a temperature-field monitoring system that achieves high temperature sensing accuracy and fast response time using commercial sound playing/recording devices. First, our system uses a distributed ranging algorithm to measure the time-of-flight of multiple sound paths with microsecond resolution. We then propose a dRadon transform algorithm that reconstructs the temperature distribution from the measured speed of sound along different paths. Our experimental resultsshow that we can measure the temperature with an error of 0.25C from single sound path and reconstruct the temperature distribution at a decimeter-level spatial resolution.

    Haoran Wan, Lei Wang, Ting Zhao, Ke Sun, Shuyu Shi, Haipeng Dai, Guihai Chen, Haodong Liu, Wei Wang
    ACM IMWUT/UbiComp, 2022 Distinguished Paper Award

    UbiComp '22
  13. 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, Sanglu Lu
    ACM IMWUT/UbiComp, 2021
    UbiComp '21
  14. RespTracker: Multi-user Room-scale Respiration Tracking with Commercial Acoustic Devices
    Abstract: Continuous domestic respiration monitoring provides vital information for diagnosing assorted diseases. In this paper, we introduce RESPTRACKER, the first continuous, multiple-person respiration tracking system in domestic settings using acoustic-based COTS devices. RESPTRACKER uses a two-stage algorithm to separate and recombine respiration signals from multiple paths in short period so that it can track the respiration rate of multiple moving subjects. Our experimental results show that our two-stage algorithm can distinguish the respiration of at least four subjects at a distance of 3 meters.

    Haoran Wan, Shuyu Shi, Wenyu Cao, Wei Wang, and Guihai Chen
    IEEE INFOCOM, 2021
    INFOCOM '21
  15. Retwork: Exploring Reader Network with COTS RFID Systems

    Jia Liu, Xingyu Chen, Shigang Chen, Wei Wang, Dong Jiang, and Lijun chen
    USENIX ATC, 2020
    ATC '20
  16. Dynamic Speed Warping: Similarity-Based One-shot Learning for Device-free Gesture Signals
    Abstract: In this paper, we propose a Dynamic Speed Warping(DSW) algorithm to enable one-shot learning for device-free gesture signals performed by different users. The design of DSW is based on the observation that the gesture type is determined by the trajectory of hand components rather than the movement speed. By dynamically scaling the speed distribution and tracking the movement distance along the trajectory, DSW can effectively match gesture signals from different domains that have a ten-fold difference in speeds. Our experimental results show that DSW can achieve a recognition accuracy of 97% for gestures performed by unknown users, while only use one training sample of each gesture type from four training users.

    Xun Wang, Ke Sun, Ting Zhao, Wei Wang, Qing Gu
    IEEE INFOCOM, 2020
    INFOCOM '20
  17. SpiderMon: Towards Using Cell Towers as Illuminating Sources for Keystroke Monitoring
    Abstract: Cellular network operators deploy base stations with a high density to ensure radio signal coverage for 4G/5G networks. While users enjoy the high-speed connection provided by cellular networks, an adversary could exploit the dense cellular deployment to detect nearby human movements and even recognize keystroke movements of a victim by passively listening to the CRS broadcast from base stations. To demonstrate this, we develop SpiderMon, the first attempt to perform passive continuous keystroke monitoring using the signal transmitted by commercial cellular base stations. Our experimental results show that SpiderMon can detect keystrokes at a distance of 15 meters and can recover a 6-digits PIN input with a success rate of more than 51% within ten trails even when the victim is behind the wall.

    Kang Ling, Yuntang Liu, Ke Sun, Wei Wang, Lei Xie, Qing Gu
    IEEE INFOCOM, 2020
    INFOCOM '20
  18. Robust Dynamic Hand Gesture Interaction using LTE Terminals

    Weiyan Chen, Kai Niu, Deng Zhao, Rong Zheng, Dan Wu, Wei Wang, Leye Wang, Daqing Zhang
    ACM/IEEE IPSN, 2020
    IPSN '20
  19. 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 degrees in the rotation tracking in the 3D space.

    Chuyu Wang, Lei Xie, Keyan Zhang, Wei Wang, Yanling Bu, and Sanglu Lu
    IEEE INFOCOM, 2019
    INFOCOM '19
  20. Speak Based Human Authentication on Smartphones
    Abstract: Voice has been used as biometrics for human authentication because different people have different voice characteristics due to different vocal tract shapes and intonations. However, traditional voice based human authentication is subject to four types of attacks: impersonation, voice conversion, synthesis and voice replay. In this paper, we propose SpeakPrint, an ultrasound based human speech authentication scheme for smartphones which is resistant for these attacks. Compared with traditional speech authentication system which focuses on what a user speaks, SpeakPrint captures how a user speaks by recording mouth and vocal movement through ultrasound signal at the same time. Our key insight is that for the valid user, features extracted from voice signal should be consistent with his mouth and vocal movement recorded from ultrasound signal, while an imitator or an audio player can't produce the same signals in ultrasound domain. SpeakPrint extracts MFCC feature in normal voice frequency and MMSI features from ultrasound signal. An SVM classifier is trained to detect these attacks by comparing above feature differences. We implemented SpeakPrint on Samsung S5 and conducted experiments on 40 users. Experimental results show that SpeakPrint can detect replay attacks with 100% accuracy and replay attack with lip synching for 99.12% for passphrases longer than five words. This technology can be used in multi-factor authentication systems, where multiple authentication mechanisms are used to achieve defense in depth.

    Haipeng Dai, Wei Wang, Alex X. Liu, Kang Ling and Jiajun Sun
    IEEE SECON, 2019
    SECON '19
  21. PCIAS: Precise and Contactless Measurement of Instantaneous Angular Speed using a Smartphone
    Abstract: Measuring Instantaneous Angular Speed (IAS) of rotating objects is ubiquitous in our daily life. Traditional IAS measurement systems have inherent limitations in the aspect of installation, accuracy, and cost. In this paper, we propose PCIAS, a system that uses acoustic signals of a universal smartphone to precisely measure IAS of rotating objects in a contactless manner. To measure the IAS precisely, we first choose appropriate measurement range for the rotating object accroding to applications. We then use the smartphone to collect acoustic signals backscattered or generated by the rotating object. Next, we extract acoustic features of the rotating object to eliminate interferences from the environment. To achieve the goal of continuous measurement, we propose a robust tracking algorithm to estimate IAS by matching cycle time length of acoustic features adaptively. We build two testbeds to evaluate the accuracy and the robustness of our system in every IAS range respectively. Our experiments show that PCIAS ahieves a relative accuracy of more than 92% in the low IAS range, more than 94% in the middle IAS range and more than 96% in the high IAS range. Finally, we exhibit two typical cases to demonstrate the practical use of our system.

    Zeshui Li, Haipeng Dai, Wei Wang, Alex X. Liu and Guihai Chen
    ACM IMWUT/UbiComp, 2019
    UbiComp '19
  22. 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 degrees in the rotation tracking in the 3D space.

    Chuyu Wang, Lei Xie, Keyan Zhang, Wei Wang, Yanling Bu, and Sanglu Lu
    IEEE INFOCOM, 2019
    INFOCOM '19
  23. VSkin: Sensing Touch Gestures on Surfaces of Mobile Devices Using Acoustic Signals
    Abstract: Enabling touch gesture sensing on all surfaces of the mobile device, not limited to the touchscreen area, leads to new user interaction experiences. In this paper, we propose VSkin, a system that supports fine-grained gesture-sensing on the back of mobile devices based on acoustic signals. VSkin utilizes both the structure-borne sounds, i.e., sounds propagating through the structure of the device, and the air-borne sounds, i.e., sounds propagating through the air, to sense finger tapping and movements. By measuring both the amplitude and the phase of each path of sound signals, VSkin detects tapping events with an accuracy of 99.65% and captures finger movements with an accuracy of 3.59mm.

    Ke Sun, Ting Zhao, Wei Wang, and Lei Xie
    ACM MobiCom, 2018

    MobiCom '18
  24. Unlock With Your Heart: Heartbeat-based Authentication on Commercial Mobile Phones
    Abstract: In this paper, we propose to use the vibration of the chest in response to the heartbeat as a biometric feature to authenticate the user on mobile devices. We use the built-in accelerometer to capture the heartbeat signals on commercial mobile phones. The user only needs to press the phone on his/her chest, and the system can identify the user within a few heartbeats. To reliably extract heartbeat features, we design a two-step alignment scheme that can handle the natural variability in human heart rates. We further use an adaptive template selection scheme to authenticate the user under different body postures and body states. Based on heartbeat signals collected on twenty users, the experimental results show that our method can achieve an authentication accuracy of 96.49% and the heartbeat features are stable over a period of three months.

    Lei Wang, Kang Huang, Ke Sun, Wei Wang, Chen Tian, Lei Xie, and Qing Gu
    ACM IMWUT/UbiComp, 2018

    UbiComp '18
  25. Depth Aware Finger Tapping on Virtual Displays
    Abstract: For AR/VR systems, tapping-in-the-air is a user-friendly solution for interactions. Most prior in-air tapping schemes for AR/VR systems use customized depth-cameras and therefore have the limitations of low accuracy and high latency. In this paper, we propose a fine-grained depth-aware tapping scheme that can provide high accuracy tapping detection with low hardware costs. Our basic idea is to use light-weight ultrasound based sensing, along with one COTS mono-camera, to enable 3D tracking of user’s fingers. The mono-camera is used to track user’s fingers in the 2D space and light-weight ultrasound based sensing is used to get the depth information of user’s fingers in the 3D space. Using the speaker and microphones that already exist on most AR/VR devices, we emit ultrasound, which is inaudible to humans, from the speaker and capture the signal reflected by the finger with the microphone. By measuring the phase changes of the ultrasound signal, we accurately measure small finger movements in the depth direction. With fast and light-weight ultrasound signal processing algorithms, our scheme can accurately track finger movements and measure the bending angle of the finger within the gap between two video frames. By fusing information from both ultrasound and vision, our scheme achieves a 98.4% finger tapping detection accuracy with FPR of 1.6% and FNR of 1.4%, and a detection latency of 17.69ms, which is 57.7ms less than video-only schemes.

    Ke Sun, Wei Wang, Alex X. Liu, and Haipeng Dai
    ACM MobiSys, 2018
    MobiSys '18
  26. QGesture: Quantifying Gesture Distance and Direction with WiFi Signals
    Abstract: Many HCI applications, such as volume adjustment in a gaming system, require quantitative gesture measurement for metrics such as movement distance and direction. In this paper, we propose QGesture, a gesture recognition system that uses CSI values provided by COTS WiFi devices to measure the movement distance and direction of human hands. To achieve high accuracy in measurements, we first use phase correction algorithm to remove the phase noise in CSI measurements. We then propose a robust estimation algorithm, called LEVD, to estimate and remove the impact of environmental dynamics. To separate gesture movements from daily activities, we design simple gestures with unique characteristics as preambles to determine the start of the gesture. Our experimental results show that QGesture achieves an average accuracy of 3 cm in the measurement of movement distance and more than 95% accuracy in the movement direction detection in the one-dimensional case. Furthermore, it achieves an average absolute direction error of 15 degrees and an average accuracy of 3.7 cm in the measurement of movement distance in the two-dimensional case.

    Nan Yu, Wei Wang, Alex X. Liu, and Lingtao Kong
    ACM IMWUT/UbiComp, 2018
    UbiComp '18
  27. 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 hese 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
    UbiComp '18
  28. Multi-Touch in the Air: Device-Free Finger Tracking and Gesture Recognition via COTS RFID.
    Abstract: Recently, gesture recognition has gained considerablevattention in emerging applications (e.g., AR/VR systems) sto provide better user experience for human-computer interaction.vExisting solutions usually recognize the gestures based on wearable sensors or specialized far-field signals (e.g., WiFi, acoustic and visible light), but they are either incurring high energy consumption or susceptible to the ambient environment, hindering them from efficiently sensing the fine-grained finger movements. In this paper, we present RF-finger, a device-free system based on Commercial-Off-The-Shelf (COTS) RFID, which leverages a 2D RFID tag array on a letter size paper to sense the fine-grained finger movements performed in front of the paper. Particularly, we focus on two kinds of sensing modes: finger tracking recovers the moving trace of finger writings; multi-touch gesture recognition identifies the multi-touch gesture involving multiple fingers. Specifically, we build a theoretical model to extract the reflection features from the raw RF-signal, which describes the finger influence on the tag array. For the finger tracking, we leverage K-Nearest Neighbors (KNN) to pinpoint the finger relying on the reflection features, and obtain a smoothed trace via Kalman filter. Additionally, we use a Convolutional Neural Network (CNN) based model to identify the multi-touch gesture based on the reflection features. Extensive experiments validate that RF-finger can achieve as high as 88% and 92% accuracy for finger tracking and multi-touch gesture recognition,respectively.

    Chuyu Wang, Jian Liu, Yingying Chen, Hongbo Liu, Lei Xie, Wei Wang, Bingbing He, and Sanglu Lu
    IEEE INFOCOM, 2018
    INFOCOM '18
  29. Meta-Activity Recognition: A Wearable Approach for Logic Cognition-based Activity Sensing
    Abstract: Activity sensing has become a key technology for many ubiquitous applications, such as exercise monitoring. Traditional approaches track the human motions and perform activity recognition mainly based on the waveform matching schemes in the raw data level. For the complex activities with relatively large moving range, they usually fail to accurately recognize these activities, due to the inherent deviations in the human specific characters. In this paper, we propose a wearable approach for logic cognition-based activity sensing scheme in the logical representation level, by leveraging the meta-activity recognition. Our solution extracts the angle profiles to depict the angle variation of limb movement in the consistent body coordinate system. It further extracts the meta-activity profiles to depict the sequence of small range activities in the complex activity. By leveraging the least edit distance-based matching scheme, our solution is able to accurately perform the activity sensing. Based on the logic cognition-based activity sensing, our solution achieves lightweight-training recognition, which requires a small quantity of training samples to build the templates, and user-independent recognition, which requires no training from the specific user. The experiment results in real settings shows that our meta-activity recognition achieves an average accuracy of 92% for user-independent activity sensing.

    Lei Xie, Xu Dong, Wei Wang, and Dawei Huang
    IEEE INFOCOM, 2017
    INFOCOM '17
  30. Device-Free Gesture Tracking Using Acoustic Signals
    Abstract: Device-free gesture tracking is an enabling HCI mechanism for small wearable devices because fingers are too big to control the GUI elements on such small screens, and it is also an important HCI mechanism for medium-to-large size mobile devices because it allows users to provide input without blocking screen view. In this paper, we propose LLAP, a device-free gesture tracking scheme that can be deployed on existing mobile devices as software, without any hardware modification. We use speakers and microphones that already exist on most mobile devices to perform device-free tracking of a hand/finger. The key idea is to use acoustic phase to get fine-grained movement direction and movement distance measurements. LLAP first extracts the sound signal reflected by the moving hand/finger after removing the background sound signals that are relatively consistent over time. LLAP then measures the phase changes of the sound signals caused by hand/finger movements and then converts the phase changes into the distance of the movement. We implemented and evaluated LLAP using commercial-off-the-shelf mobile phones. For 1-D hand movement and 2-D drawing in the air, LLAP has a tracking accuracy of 3.5 mm and 4.6 mm, respectively. Using gesture traces tracked by LLAP, we can recognize the characters and short words drawn in the air with an accuracy of 92.3% and 91.2%, respectively.

    Wei Wang, Alex X. Liu, and Ke Sun
    ACM MobiCom, Oct 2016

    MobiCom '16
  31. Gait Recognition Using WiFi Signals
    Abstract: In this paper, we propose WifiU, which uses commercial WiFi devices to capture fine-grained gait patterns to recognize humans. The intuition is that due to the differences in gaits of different people, the WiFi signal reflected by a walking human generates unique variations in the Channel State Information (CSI) on the WiFi receiver. To profile human movement using CSI, we use signal processing techniques to generate spectrograms from CSI measurements so that the resulting spectrograms are similar to those generated by specifically designed Doppler radars. To extract features from spectrograms that best characterize the walking pattern, we perform autocorrelation on the torso reflection to remove imperfection in spectrograms. We evaluated WifiU on a dataset with 2,800 gait instances collected from 50 human subjects walking in a room with an area of 50 square meters. Experimental results show that WifiU achieves top-1, top-2, and top-3 recognition accuracies of 79.28%, 89.52%, and 93.05%, respectively.

    Wei Wang, Alex X. Liu, and Muhammad Shahzad
    ACM UbiComp, Sep 2016
    UbiComp '16
  32. Noisy Bloom Filters for Multi-Set Membership Testing
    Abstract: This paper is on designing a compact data structure for multi-set membership testing allowing fast set querying. Multi-set membership testing is a fundamental operation for computing systems and networking applications. Most existing schemes for multi-set membership testing are built upon Bloom filter, and fall short in either storage space cost or query speed. To address this issue, in this paper we propose Noisy Bloom Filter (NBF) and Error Corrected Noisy Bloom Filter (NBF-E) for multi-set membership testing. For theoretical analysis, we optimize their classification failure rate and false positive rate, and present criteria for selection between NBF and NBF-E. The key novelty of NBF and NBF-E is to store set ID information in a compact but noisy way that allows fast recording and querying, and use denoising method for querying. Especially, NBF-E incorporates asymmetric error-correcting coding technique into NBF to enhance the resilience of query results to noise by revealing and leveraging the asymmetric error nature of query results. To evaluate NBF and NBF-E in comparison with prior art, we conducted experiments using real-world network traces. The results show that NBF and NBF-E significantly advance the state-of-the-art on multi-set membership testing.

    HaiPeng Dai, Yuankun Zhong, Alex Liu, Wei Wang, and Meng Li
    ACM SIGMETRICS, 2016
    SIGMETRICS '16
  33. Moving Tag Detection via Physical Layer Analysis for Large-Scale RFID Systems
    Abstract: In a number of RFID-based applications such as logistics monitoring, the RFID systems are deployed to monitor a large number of RFID tags. They are usually required to track the movement of all tags in a real-time approach, since the tagged-goods are moved in and out in a rather frequent approach. However, a typical cycle of tag inventory in COTS RFID system usually takes tens of seconds to interrogate hundreds of RFID tags. This hinders the system to track the movement of all tags in time. One critical issue in such type of tag monitoring is to efficiently distinguish the motion status of all tags, i.e., stationary or moving. According to the motion status of different tags, the state-of-art localization schemes can further track those moving tags, instead of tracking all tags. In this paper, we propose a real-time approach to detect the moving tags in the monitoring area, which is a fundamental premise to support tracking the movement of all tags. We achieve the time efficiency by decoding collisions from the physical layer. Instead of using the EPC ID, which cannot be decoded in collision slots, we are able to extract two kinds of physical-layer features of RFID tags, i.e., the phase profile and the backscatter link frequency, to distinguish among different tags in different positions. By resolving the two physicallayer features from the tag collisions, we are able to derive the motion status of multiple tags simultaneously, and greatly improve the time-efficiency. Experiment result shows that our solution can accurately detect the moving tags while reducing 80% of inventory time compared with the state-of-art solutions.

    Chuyu Wang, Lei Xie, Wei Wang, and Sanglu Lu
    IEEE INFOCOM, 2016
    INFOCOM '16
  34. Understanding and Modeling of WiFi Signal Based Human Activity Recognition
    Abstract: Some pioneer WiFi signal based human activity recognition systems have been proposed. Their key limitation lies in the lack of a model that can quantitatively correlate CSI dynamics and human activities. In this paper, we propose CARM, a CSI based human Activity Recognition and Monitoring system. CARM has two theoretical underpinnings: a CSI-speed model, which quantifies the correlation between CSI value dynamics and human movement speeds, and a CSI-activity model, which quantifies the correlation between the movement speeds of different human body parts and a specific human activity. By these two models, we quantitatively build the correlation between CSI value dynamics and a specific human activity. CARM uses this correlation as the profiling mechanism and recognizes a given activity by matching it to the best-fit profile. We implemented CARM using commercial WiFi devices and evaluated it in several different environments. Our results show that CARM achieves an average accuracy of greater than 96%.

    Wei Wang, Alex X. Liu, Muhammad Shahzad, Kang Ling, and Sanglu Lu
    ACM MobiCom, Sep 2015

    MobiCom '15
  35. Keystroke Recognition Using WiFi Signals
    Abstract: Keystroke privacy is critical for ensuring the security of computer systems and the privacy of human users as what being typed could be passwords or privacy sensitive information. In this paper, we show for the first time that WiFi signals can also be exploited to recognize keystrokes. The intuition is that while typing a certain key, the hands and fingers of a user move in a unique formation and direction and thus generate a unique pattern in the time-series of Channel State Information (CSI) values, which we call CSI-waveform for that key. In this paper, we propose a WiFi signal based keystroke recognition system called WiKey. WiKey consists of two Commercial Off-The-Shelf (COTS) WiFi devices, a sender (such as a router) and a receiver (such as a laptop). The sender continuously emits signals and the receiver continuously receives signals. When a human subject types on a keyboard, WiKey recognizes the typed keys based on how the CSI values at the WiFi signal receiver end. We implemented the WiKey system using a TP-Link TL-WR1043ND WiFi router and a Lenovo X200 laptop. WiKey achieves more than 97.5% detection rate for detecting the keystroke and 96.4% recognition accuracy for classifying single keys. In real-world experiments, WiKey can recognize keystrokes in a continuously typed sentence with an accuracy of 93.5%.

    Kamran Ali, Alex X. Liu, Wei Wang, and Muhammad Shahzad
    ACM MobiCom, Sep 2015
    MobiCom '15
  36. Femto-Matching: Efficient Traffic Offloading in Heterogeneous Cellular Networks
    Abstract: Heterogeneous cellular networks use small base stations, such as femtocells and WiFi APs, to offload traffic from macrocells. While network operators wish to globally balance the traffic, users may selfishly select the nearest base stations and make some base stations overcrowded. In this paper, we propose to use an auction-based algorithm - Femto-Matching, to achieve both load balancing among base stations and fairness among users. Femto-Matching optimally solves the global proportional fairness problem in polynomial time by transforming it into an equivalent matching problem. Furthermore, it can efficiently utilize the capacity of randomly deployed small cells. Our tracedriven simulations show Femto-Matching can reduce the load of macrocells by more than 30% compared to non-cooperative game based strategies.

    Wei Wang, Xiaobing Wu, Lei Xie, and Sanglu Lu
    IEEE INFOCOM, Apr 2015
    INFOCOM '15

Journals

  1. SCALAR: Self-Calibrated Acoustic Ranging for Distributed Mobile Devices
    Abstract: Acoustic ranging has been viewed as a promising Human-Computer Interaction (HCI) technology in many scenarios, such as Augmented Reality (AR)/Virtual Reality (VR) and smart appliances. Most ranging systems with distributed devices undergo an extra calibration process to remove the timing errors. However, the calibration process needs user intervention. Furthermore, it should assume that the clock drifts are linear and stable, which is disabled within tens of minutes. In this paper, we introduce a self-calibrated acoustic ranging system that achieves sub-millimeter accuracy on distributed asynchronous devices. Based on our theoretical timing model, we precisely cancel both the system delay and the nonlinear clock drift with carefully designed Orthogonal Frequency-Division Multiplexing (OFDM) ranging signals. Our synchronization scheme achieves a timing accuracy of 1.9 microseconds, which allows us to build large-scale virtual acoustic arrays. Based on such a calibration scheme, our localization system achieves a ranging error of 0.39 mm within three meters in real-world experiments.

    Lei Wang, Haoran Wan, Ting Zhao, Ke Sun, Shuyu Shi, Haipeng Dai, Guihai Chen, Haodong Liu, Wei Wang
    IEEE Transactions on Mobile Computing, 2023
    TMC
  2. Multi-user Room-scale Respiration Tracking using COTS Acoustic Devices
    Abstract: Continuous domestic respiration monitoring provides vital information for diagnosing assorted diseases. In this paper, we introduce RespTracker, the first continuous, multiple-person respiration tracking system in domestic settings using acoustic-based COTS devices. RespTracker uses a multi-stage algorithm to separate and recombine respiration signals from multiple paths so that it can track the respiration rate of multiple moving subjects. And it leverages features from multiple dimensions to separate different users in the same area. Our experimental results show that our two-stage algorithm can distinguish the respiration of at least four subjects and cover a distance of three meters.

    Haoran Wan, Shuyu Shi, Wenyu Cao, Wei Wang, and Guihai Chen
    ACM Transactions on Sensor Networks, 2023
    Extended version of INFOCOM '21 paper
    TOSN
  3. DSW: One-shot Learning Scheme for Device-free Acoustic Gesture Signals
    Abstract: In this paper, we propose a Dynamic Speed Warping (DSW) algorithm to enable one-shot learning for device-free acoustic gesture signals performed by different users. The design of DSW is based on the observation that the gesture type is determined by the trajectory of hand components rather than the movement speed. By dynamically scaling the speed distribution and tracking the movement distance along the trajectory, DSW can effectively match gesture signals from different domains with a ten-fold difference in speeds. Our experimental results show that DSW can achieve a recognition accuracy of 97% for gestures performed by unknown users while only using one training sample of each gesture type from four training users.

    Xun Wang, Ke Sun, Ting Zhao, Wei Wang, Qing Gu
    IEEE Transactions on Mobile Computing, 2023
    Extended version of INFOCOM'20 paper
    TMC
  4. Bloom Filter with Noisy Coding Framework for Multi-Set Membership Testing
    Abstract: This paper is on designing a compact data structure for multi-set membership testing that allows fast set querying. Multi-set membership testing is a fundamental operation for computing systems. Most existing schemes for multi-set membership testing are built upon Bloom filter and fall short in either storage space cost or query speed. To address this issue, we propose Noisy Bloom Filter (NBF), Error Corrected Noisy Bloom Filter (NBF-E), and Data-driven Noisy Bloom Filter (NBF-D) in this paper. We optimize their misclassification and false positive rates by theoretical analysis and present criteria for selection between NBF, NBF-E, and NBF-D. The key novelty of the three schemes is to store set ID information in a compact but noisy way that allows fast recording and querying and use a denoising method for querying. Especially, NBF-E incorporates asymmetric error-correcting coding techniques into NBF, and NBF-D encodes set ID based on their cardinality. To evaluate NBF, NBF-E, and NBF-D in comparison with the prior art, we conducted experiments using real-world network traces. The results show that NBF, NBF-E, and NBF-D significantly advance the state-of-the-art on multi-set membership testing.

    Haipeng Dai, Jun Yu, Meng Li, Wei Wang, Jinghao Ma, Lianyong Qi, Alex X. Liu and Guihai Chen
    IEEE Transactions on Knowledge and Data Engineering, 2022
    TKDE
  5. UltraGesture: Fine-Grained Gesture Sensing and Recognition
    Abstract: With the rising of AR/VR technology and miniaturization of mobile devices, gesture is becoming an increasingly popular means of interacting with smart devices. Some pioneer ultrasound-based human gesture recognition systems have been proposed. They mostly rely on low-resolution Doppler Effect, and hence focus on whole hand motion and cannot deal with minor finger motions. In this paper, we present UltraGesture, a Channel Impulse Response (CIR) based ultrasonic finger motion perception and recognition system. CIR measurements can provide with 7 mm resolution, rendering it sufficient for minor finger motion recognition. UltraGesture encapsulates CIR measurements into an image, and builds a Convolutional Neural Network model to classify these images into different categories, which corresponding to distinct gestures. Furthermore, we use a sliding-window based method to improve accuracy and reduce response latency. Our system runs on commercial speakers and microphones that already exist on most mobile devices without hardware modification. Our results show that UltraGesture achieves an average accuracy of greater than 99% for 12 gestures including finger click and rotation.

    Kang Ling, Haipeng Dai, Yuntang Liu, Alex X. Liu, Wei Wang, Qing Gu
    IEEE Transactions on Mobile Computing, 2022
    Extended version of SECON '18 paper
    TMC
  6. SpeedTalker: Automobile Speed Estimation via Mobile Phones
    Abstract: Among all the road accidents, speeding is the most deadly factor. To reduce speeding, it is essential to devise efficient schemes for ubiquitous speed monitoring. Traditional approaches either suffers from using special equipment or special deployment. In this paper, we propose SpeedTalker, a mobile phone-based approach to perform speed detection on automobiles. SpeedTalker estimates the automobile speed by passively sensing the acoustic and image signals. Specifically, we use the time difference of arrivals (TDOA) model based on acoustic signals to figure out the candidate trajectories of automobile, and use the pin-hole model based on image frames to figure out the vertical distance between the user's position and the automobile's trajectory, thus to estimate the unique trajectory. Besides, we propose a method to effectively mitigate the influence of the movement jitters of mobile phone. We implemented a system prototype for SpeedTalker and estimated the automobile speed with high accuracy. Experiment results show that in the scenario of single automobile, SpeedTalker can achieve an average estimation error of 6.1% compared to radar speed guns. In the scenario of multiple automobiles, SpeedTalker can achieve an average estimation error of 9.8%, which is acceptable for usage.

    Xinran Lu, Lei Xie, Yafeng Yin, Wei Wang, Yanling Bu, Qing Guo, Sanglu Lu
    IEEE Transactions on Mobile Computing, 2022
    TMC
  7. WiTrace: Centimeter-Level Passive Gesture Tracking Using OFDM signals
    Abstract: Gesture tracking is a basic Human-Computer Interaction mechanism to control devices such as IoT and VR/AR devices. However, prior OFDM signal based systems focus on gesture recognition and provide results with insufficient accuracy, and thus cannot be applied for high-precision gesture tracking. In this paper, we propose a CSI based device-free gesture tracking system, called WiTrace, which leverages the CSI values extracted from OFDM signals to enable accurate gesture tracking. For 1D tracking, WiTrace derives the phase of the signals reflected by the hand from the composite signals, and measures the phase changes to obtain the movement distance. For 2D tracking, WiTrace proposes the first CSI based scheme to accurately estimate the initial position, and adopts the Kalman Filter based on continuous Wiener process acceleration model to further filter out tracking noise. Our results show that WiTrace achieves an average accuracy of 6.23 cm for initial position estimation, and achieves cm-level accuracy, with average tracking errors of 1.46 cm and 2.09 cm for 1D tracking and 2D tracking, respectively.

    Lei Wang, Ke Sun, Haipeng Dai, Wei Wang, Kang Huang, Alex X. Liu, Xiaoyu Wang, Qing Gu
    IEEE Transactions on Mobile Computing, 2021
    Extended version of SECON '18 paper
    TMC
  8. Exploring Token-oriented In-network Prioritization in Datacenter Networks
    Abstract: In-memory computing and high-end distributed storage demand low latency, high throughput, and zero data losssimultaneously from datacenter networks. Existing reactive congestion control approaches cannot both minimize queuing latency andensure zero data loss. A token-oriented proactive approach can achieve them together by controlling congestion even before sendingdata packets. However, state-of-the-art token-oriented approaches only strive to optimize network-level metrics: maximizing throughputwhile achieving flow-level fairness. This paper answers the question of how to support objective-aware traffic scheduling intoken-oriented approaches. The novelty of Token-Oriented in-network Prioritization (TOP) is that it prioritizes tokens instead of datapackets. We make three contributions. Via simulations over a hypothetical TOP system, our first contribution is demonstrating thepotential performance gain that can be brought by TOP. Secondly, we investigate the applicability of TOP. Although the overhead ofenabling necessary TOP features in switches is trivial, we find that mainstream commodity datacenter switches do not support them. We hence propose a readily-deployable remedy to achieve in-network prioritization by pushing both switch and end-host hardware capacity to an extreme end. Lastly, we implement a running TOP system with Linux hosts and commodity switches, and evaluate TOP in testbeds and with large-scale simulations for various scenarios.

    Kexin Liu, Bingchuan Tian, Chen Tian, Bo Li, Qingyue Wang, Jiaqi Zheng, Jiajun Sun, Yixiao Gao, Wei Wang, Guihai Chen, Wanchun Dou, Yanan Jiang, Huaping Zhou, Jingjie Jiang, Fan Zhang, and Gong Zhang
    IEEE Transactions on Parallel and Distributed Systems, 2020
    TPDS
  9. Probing into the Physical Layer: Moving Tag Detection for Large-Scale RFID
    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, 2019
    Extended version of INFOCOM '16 paper
    TMC
  10. Synchronize Inertial Readings from Multiple Mobile Devices in Spatial Dimension
    Abstract: In this paper, we define the concept of space synchronization for mobile devices. Space synchronization can be the enabling technology for many applications such as motion sensing in virtual reality and human computer interaction. Although time synchronization, which synchronizes multiple devices so that they have the same time, have been well studied and standardized, To the best of our knowledge, space synchronization has never been formally defined and systematically studied in prior work. In this paper, we propose a scheme called MObile Space Synchronization (MOSS) for devices with two sensors: an accelerometer and a gyroscope, which are available on most mobile devices. Accelerometer readings from multiple mobile devices on a human subject are used to achieve space synchronization when the human subject is moving forward, such as walking and running. Gyroscope readings from multiple mobile devices on a human subject are used to maintain space synchronization when the human subject stops moving forward, which means that we can no longer obtain the consistent acceleration caused by body moving forward. We implemented our MOSS system on six mobile devices including one Google Glass and five Samsung S5 smart phones. Experiment results show that our MOSS scheme can achieve an average angle deviation of 9.8 degrees and an average measurement similarity of 97%.

    Lei Xie, Qingliang Cai, Alex X. Liu, Wei Wang, YafengYin, and Sanglu Lu
    IEEE/ACM Transactions on Networking, Vol. 26, no.5, Oct 2018
    TON
  11. Device-free Human Activity Recognition Using Commercial WiFi Devices
    Abstract: Since human bodies are good reflectors of wireless signals, human activities can be recognized by monitoring changes in WiFi signals. However, existing WiFi based human activity recognition systems do not build models that can quantify the correlation between WiFi signal dynamics and human activities. In this paper, we propose CARM, a Channel State Information (CSI) based human Activity Recognition and Monitoring system. CARM is based on two theoretical models. First, we propose a CSI-speed model that quantifies the relation between CSI dynamics and human movement speeds. Second, we propose a CSI-activity model that quantifies the relation between human movement speeds and human activities. Based on these two models, we implemented CARM on commercial WiFi devices. Our experimental results show that CARM achieves recognition accuracy of 96% and is robust to environmental changes.

    Wei Wang, Alex X. Liu, Muhammad Shahzad, Kang Ling, and Sanglu Lu
    IEEE JSAC, 2017
    Extended version of Mobicom '15 paper
    JSAC
  12. Recognizing Keystrokes Using WiFi Devices
    Abstract: Keystroke privacy is critical for ensuring the security of computer systems and the privacy of human users as what being typed could be passwords or privacy sensitive information. In this paper, we show for the first time that WiFi signals can also be exploited to recognize keystrokes. The intuition is that while typing a certain key, the hands and fingers of a user move in a unique formation and direction and thus generate a unique pattern in the time-series of Channel State Information (CSI) values, which we call CSI-waveform for that key. In this paper, we propose a WiFi signal based keystroke recognition system called WiKey.WiKey consists of two Commercial Off-The-Shelf (COTS) WiFi devices, a sender (such as a router) and a receiver (such as a laptop). The sender continuously emits signals and the receiver continuously receives signals. When a human subject types on a keyboard, WiKey recognizes the typed keys based on how the CSI values at the WiFi signal receiver end. We implemented the WiKey system using a TP-Link TL-WR1043ND WiFi router and a Lenovo X200 laptop. WiKey achieves more than 97.5% detection rate for detecting the keystroke and 96.4% recognition accuracy for classifying single keys. In real-world experiments, WiKey can recognize keystrokes in a continuously typed sentence with an accuracy of 93.5%. WiKey can also recognize complete words inside a sentence with more than 85% accuracy.

    Kamran Ali, Alex X. Liu, Wei Wang, and Muhammad Shahzad
    IEEE JSAC, 2017
    Extended version of Mobicom '15 paper
    JSAC
  13. Opportunistic Energy Efficient Contact Probing in Delay Tolerant Applications
    Abstract: In many delay-tolerant applications, information is opportunistically exchanged between mobile devices that encounter each other. In order to affect such information exchange, mobile devices must have knowledge of other devices in their vicinity. We consider scenarios in which there is no infrastructure and devices must probe their environment to discover other devices. This can be an extremely energy-consuming process and highlights the need for energy-conscious contact-probing mechanisms. If devices probe very infrequently, they might miss many of their contacts. On the other hand, frequent contact probing might be energy inefficient. In this paper, we investigate the tradeoff between the probability of missing a contact and the contact-probing frequency. First, via theoretical analysis, we characterize the tradeoff between the probability of a missed contact and the contact-probing interval for stationary processes. Next, for time-varying contact arrival rates, we provide an optimization framework to compute the optimal contact-probing interval as a function of the arrival rate. We characterize real-world contact patterns via Bluetooth phone contact-logging experiments and show that the contact arrival process is self-similar. We design STAR, a contact-probing algorithm that adapts to the contact arrival process. Instead of using constant probing intervals, STAR dynamically chooses the probing interval using both the short-term contact history and the long-term history based on time of day information. Via trace-driven simulations on our experimental data, we demonstrate that STAR requires threeto five times less energy for device discovery than a constant contact-probing interval scheme.

    Wei Wang, Mehul Motani, and Vikram Srinivasan
    IEEE/ACM Transactions on Networking,Vol.17, no.5, Oct 2009
    Extended version of Mobicom '07 paper
    TON

 

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