Wei Wang    chinese


Nanjing University

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

Office Hours: Every Thursday 14:00 - 16:00

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. 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 (to appear)
    SECON '19
  2. 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 (to appear)
    UbiComp '19
  3. 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 (to appear)
    INFOCOM '19
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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. 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
  2. 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
  3. 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
  4. 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
  5. 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

 
Note: tags with borders, such as MobiCom '07 , indicate that the first author is my student or me.

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