Jian Liu

Bio: Jian Liu is an academic researcher from University of Tennessee. The author has contributed to research in topics: Wearable computer & Authentication. The author has an hindex of 21, co-authored 88 publications receiving 2095 citations. Previous affiliations of Jian Liu include Wuhan University of Technology & Binghamton University.

E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures

Abstract: Activity monitoring in home environments has become increasingly important and has the potential to support a broad array of applications including elder care, well-being management, and latchkey child safety. Traditional approaches involve wearable sensors and specialized hardware installations. This paper presents device-free location-oriented activity identification at home through the use of existing WiFi access points and WiFi devices (e.g., desktops, thermostats, refrigerators, smartTVs, laptops). Our low-cost system takes advantage of the ever more complex web of WiFi links between such devices and the increasingly fine-grained channel state information that can be extracted from such links. It examines channel features and can uniquely identify both in-place activities and walking movements across a home by comparing them against signal profiles. Signal profiles construction can be semi-supervised and the profiles can be adaptively updated to accommodate the movement of the mobile devices and day-to-day signal calibration. Our experimental evaluation in two apartments of different size demonstrates that our approach can achieve over 96% average true positive rate and less than 1% average false positive rate to distinguish a set of in-place and walking activities with only a single WiFi access point. Our prototype also shows that our system can work with wider signal band (802.11ac) with even higher accuracy.

Tracking Vital Signs During Sleep Leveraging Off-the-shelf WiFi

Abstract: Tracking human vital signs of breathing and heart rates during sleep is important as it can help to assess the general physical health of a person and provide useful clues for diagnosing possible diseases. Traditional approaches (e.g., Polysomnography (PSG)) are limited to clinic usage. Recent radio frequency (RF) based approaches require specialized devices or dedicated wireless sensors and are only able to track breathing rate. In this work, we propose to track the vital signs of both breathing rate and heart rate during sleep by using off-the-shelf WiFi without any wearable or dedicated devices. Our system re-uses existing WiFi network and exploits the fine-grained channel information to capture the minute movements caused by breathing and heart beats. Our system thus has the potential to be widely deployed and perform continuous long-term monitoring. The developed algorithm makes use of the channel information in both time and frequency domain to estimate breathing and heart rates, and it works well when either individual or two persons are in bed. Our extensive experiments demonstrate that our system can accurately capture vital signs during sleep under realistic settings, and achieve comparable or even better performance comparing to traditional and existing approaches, which is a strong indication of providing non-invasive, continuous fine-grained vital signs monitoring without any additional cost.

Smart User Authentication through Actuation of Daily Activities Leveraging WiFi-enabled IoT

Abstract: User authentication is a critical process in both corporate and home environments due to the ever-growing security and privacy concerns. With the advancement of smart cities and home environments, the concept of user authentication is evolved with a broader implication by not only preventing unauthorized users from accessing confidential information but also providing the opportunities for customized services corresponding to a specific user. Traditional approaches of user authentication either require specialized device installation or inconvenient wearable sensor attachment. This paper supports the extended concept of user authentication with a device-free approach by leveraging the prevalent WiFi signals made available by IoT devices, such as smart refrigerator, smart TV and thermostat, etc. The proposed system utilizes the WiFi signals to capture unique human physiological and behavioral characteristics inherited from their daily activities, including both walking and stationary ones. Particularly, we extract representative features from channel state information (CSI) measurements of WiFi signals, and develop a deep learning based user authentication scheme to accurately identify each individual user. Extensive experiments in two typical indoor environments, a university office and an apartment, are conducted to demonstrate the effectiveness of the proposed authentication system. In particular, our system can achieve over 94% and 91% authentication accuracy with 11 subjects through walking and stationary activities, respectively.

Wireless Sensing for Human Activity: A Survey

Abstract: With the advancement of wireless technologies and sensing methodologies, many studies have shown the success of re-using wireless signals (e.g., WiFi) to sense human activities and thereby realize a set of emerging applications, ranging from intrusion detection, daily activity recognition, gesture recognition to vital signs monitoring and user identification involving even finer-grained motion sensing. These applications arguably can brace various domains for smart home and office environments, including safety protection, well-being monitoring/management, smart healthcare and smart-appliance interaction. The movements of the human body impact the wireless signal propagation (e.g., reflection, diffraction and scattering), which provide great opportunities to capture human motions by analyzing the received wireless signals. Researchers take the advantage of the existing wireless links among mobile/smart devices (e.g., laptops, smartphones, smart thermostats, smart refrigerators and virtual assistance systems) by either extracting the ready-to-use signal measurements or adopting frequency modulated signals to detect the frequency shift. Due to the low-cost and non-intrusive sensing nature, wireless-based human activity sensing has drawn considerable attention and become a prominent research field over the past decade. In this paper, we survey the existing wireless sensing systems in terms of their basic principles, techniques and system structures. Particularly, we describe how the wireless signals could be utilized to facilitate an array of applications including intrusion detection, room occupancy monitoring, daily activity recognition, gesture recognition, vital signs monitoring, user identification and indoor localization. The future research directions and limitations of using wireless signals for human activity sensing are also discussed.

Snooping Keystrokes with mm-level Audio Ranging on a Single Phone

Abstract: This paper explores the limits of audio ranging on mobile devices in the context of a keystroke snooping scenario. Acoustic keystroke snooping is challenging because it requires distinguishing and labeling sounds generated by tens of keys in very close proximity. Existing work on acoustic keystroke recognition relies on training with labeled data, linguistic context, or multiple phones placed around a keyboard --- requirements that limit usefulness in an adversarial context. In this work, we show that mobile audio hardware advances can be exploited to discriminate mm-level position differences and that this makes it feasible to locate the origin of keystrokes from only a single phone behind the keyboard. The technique clusters keystrokes using time-difference of arrival measurements as well as acoustic features to identify multiple strokes of the same key. It then computes the origin of these sounds precise enough to identify and label each key. By locating keystrokes this technique avoids the need for labeled training data or linguistic context. Experiments with three types of keyboards and off-the-shelf smartphones demonstrate scenarios where our system can recover $94\%$ of keystrokes, which to our knowledge, is the first single-device technique that enables acoustic snooping of passwords.

Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons

Abstract: The growing commercial interest in indoor location-based services (ILBS) has spurred recent development of many indoor positioning techniques. Due to the absence of global positioning system (GPS) signal, many other signals have been proposed for indoor usage. Among them, Wi-Fi (802.11) emerges as a promising one due to the pervasive deployment of wireless LANs (WLANs). In particular, Wi-Fi fingerprinting has been attracting much attention recently because it does not require line-of-sight measurement of access points (APs) and achieves high applicability in complex indoor environment. This survey overviews recent advances on two major areas of Wi-Fi fingerprint localization: advanced localization techniques and efficient system deployment. Regarding advanced techniques to localize users, we present how to make use of temporal or spatial signal patterns, user collaboration, and motion sensors. Regarding efficient system deployment, we discuss recent advances on reducing offline labor-intensive survey, adapting to fingerprint changes, calibrating heterogeneous devices for signal collection, and achieving energy efficiency for smartphones. We study and compare the approaches through our deployment experiences, and discuss some future directions.

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%.

A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

Abstract: The Internet of Things (IoT) is the next era of communication. Using the IoT, physical objects can be empowered to create, receive, and exchange data in a seamless manner. Various IoT applications focus on automating different tasks and are trying to empower the inanimate physical objects to act without any human intervention. The existing and upcoming IoT applications are highly promising to increase the level of comfort, efficiency, and automation for the users. To be able to implement such a world in an ever-growing fashion requires high security, privacy, authentication, and recovery from attacks. In this regard, it is imperative to make the required changes in the architecture of the IoT applications for achieving end-to-end secure IoT environments. In this paper, a detailed review of the security-related challenges and sources of threat in the IoT applications is presented. After discussing the security issues, various emerging and existing technologies focused on achieving a high degree of trust in the IoT applications are discussed. Four different technologies, blockchain, fog computing, edge computing, and machine learning, to increase the level of security in IoT are discussed.