The ever-growing security issues in various mobile applications and smart devices create an urgent demand for a reliable and convenient user verification method. Traditional verification methods request users to provide their secrets (e.g., entering passwords, perform gestures, and collect fingerprints). We envision that the essential trend of user verification is to free users from active participation in the verification process. Toward this end, we propose a continuous user verification system, which re-uses the widely deployed WiFi infrastructure to capture the unique physiological characteristics rooted in respiratory motions. Different from the existing continuous verification approaches, posing dependency on restricted scenarios/user behaviors (e.g., keystrokes and gaits), our system can be easily integrated into any WiFi infrastructure to provide non-intrusive continuous verification. Specifically, we extract the respiration-related signals from the channel state information (CSI) of WiFi. We then derive the user-specific respiratory features based on the waveform morphology analysis and fuzzy wavelet transformation of the respiration signals. Additionally, a deep learning based user verification scheme is developed to identify legitimate users accurately and detect the existence of spoofing attacks. Extensive experiments involving 20 participants demonstrate that the proposed system can robustly verify/identify users and detect spoofers under various types of attacks.

To enable safe and effective fitness in the gym, the promising Human-Computer Interaction (HCI) techniques have been applied to monitor and evaluate the fitness activities. Prior works based on wearable sensors or wireless signals (e.g., WiFi and RFID) for activity recognition can perceive the motion, but they cannot be applied in multi-person scenarios because human identity is hard to be recognized by wireless sensing techniques. The Computer Vision (CV) technique performs pretty well in recognizing human identity while it has difficulty in distinguishing the similar but actually different exercise apparatuses, e.g., dumbbells and barbells with different weights. Clearly, these two types of techniques are complementary to each other. To overcome the aforementioned limitations, this paper presents DEEM, the first deeper exercise monitoring system based on multi-modal perception technology. With the integration of RFID technology and CV technique, DEEM provides not only the exercise data, but also which object the user is holding and who is the real actor. We implement our system with COTS Kinect camera and RFID devices. Extensive experiments have been conducted to evaluate the performance of our system. The experimental results illustrate that the matching accuracy can reach 95%, and estimation accuracy can reach 94% on average.

In this work, we propose Over-The-Air (OTA)-based reconfigurable IoT health-monitoring wearables, which tether wirelessly to a low-power and portable central processing and communication hub (CPH). This hub is responsible for the proper packetization and transmission of the physiological data received from the individual sensors connected to each wearable to a remote server. Each wearable consists of a sensor, a communication adapter, and its power module. We introduce low-power adapters with each sensor, which facilitates the sensor-CPH linkups and on-demand network parameter reconfigurations. The newly introduced adapter supports the interoperability of heterogeneous sensors by eradicating the need for sensor-specific modules through OTA-based reconfiguration. The reconfiguration feature allows for new sensors to connect to an existing adapter, without changing the hardware units or any external interface. Our implemented system is highly scalable and enables multiple sensors to connect in a network and work in synchronization with the CPH to achieve semantic and device interoperability among the sensors. We test our implementation in real-time using three different health-monitoring sensor types -- temperature, pulse oximeter, and ECG. The results of our real-time system evaluation show that our system is highly reliable and responsive in terms of the achieved network metrics.

Traditional one-time user authentication processes might cause friction and unfavorable user experience in many widely-used applications. This is a severe problem in particular for security-sensitive facilities if an adversary could obtain unauthorized privileges after a user's initial login. Recently, continuous user authentication (CA) has shown its great potential by enabling seamless user authentication with few active participation. We devise a low-cost system exploiting a user's pulsatile signals from the photoplethysmography (PPG) sensor in commercial wrist-worn wearables for CA. Compared to existing approaches, our system requires zero user effort and is applicable to practical scenarios with non-clinical PPG measurements having motion artifacts (MA). We explore the uniqueness of the human cardiac system and design an MA filtering method to mitigate the impacts of daily activities. Furthermore, we identify general fiducial features and develop an adaptive classifier using the gradient boosting tree (GBT) method. As a result, our system can authenticate users continuously based on their cardiac characteristics so little training effort is required. Experiments with our wrist-worn PPG sensing platform on 20 participants under practical scenarios demonstrate that our system can achieve a high CA accuracy of over 90% and a low false detection rate of 4% in detecting random attacks.

There have been increasing interests in exploring the sensing capabilities of RFID to enable numerous IoT applications, including object localization, trajectory tracking, and human behavior sensing. However, most existing methods rely on the signal measurement either in a low multipath environment, which is unlikely to exist in many practical situations, or with special devices, which increase the operating cost. This paper investigates the possibility of measuring 'multi-path-free' signal information in multipath-prevalent environments simply using a commodity RFID reader. The proposed solution, Clean Physical Information Extraction (CPIX), is universal, accurate, and compatible to standard protocols and devices. CPIX improves RFID sensing quality with near zero cost - it requires no extra device. We implement CPIX and study two major RFID sensing applications: tag localization and human behavior sensing. CPIX reduces the localization error by 30% to 50% and achieves the MOST accurate localization by commodity readers compared to existing work. It also significantly improves the quality of human behaviour sensing.

In this paper, we introduce technology-independent ambient backscatter systems where a backscatter tag utilizes all single-stream ambient signals transmitted by nearby devices. Specifically, we design a phase demodulation algorithm that detects a backscatter signal from the phase difference between the two antennas, no matter which signal the tag reflects. We then develop a parallelized backscatter receiver that mitigates the dead spot problem by leveraging antenna diversity. To show the feasibility of our design, we implement a backscatter receiver on the software-defined radio platform and analyze 50 MHz RF bandwidth in real-time. Our evaluation shows that the receiver can decode backscatter bits carried by any single stream ambient signal such as a continuous wave, a QAM signal, and even a noise signal. We also demonstrate backscatter transmissions with commodity Wi-Fi and Bluetooth devices to prove that our design can be combined with existing wireless networks.

Mechanical vibration sensing/monitoring plays a critical role in today's industrial Internet of Things (IoT) applications. Existing solutions usually involve directly attaching sensors to the target objects, which is invasive and may affect the operations of the underlying devices. Non-invasive approaches such as video and laser methods have drawbacks in that, the former incurs poor performance in low light conditions, while the latter has difficulties to monitor multiple objects simultaneously.

In this work, we design RF-Ear, a contactless vibration sensing system using Commercial off-the-shelf(COTS) RFID hardware. RF-Ear could accurately monitor the mechanical vibrations of multiple devices (up to 8) using a single tag: it can clearly tell which object is vibrating at what frequency without attaching tags on any device. RF-Ear can measure the vibration frequency up to 400Hz with a mean error rate of 0.2%. Our evaluation results show that RF-Ear can effectively detect 2mm vibration amplitude change with 90% accuracy. We further employ each device's unique vibration fingerprint to identify and differentiate devices of exactly the same model. Comprehensive experiments conducted in a real power plant demonstrate the effectiveness of our system with great performance.

RFID technology has recently been exploited for not only identification but also for sensing including trajectory tracking and gesture recognition. While contact-based~(an RFID tag is attached to the target of interest) sensing has achieved promising results, contactless-based sensing still faces severe challenges such as low accuracy and the situation gets eve worse when the target is non-static, restricting its applicability in real world deployment. In this work, we present TagRay, a contactless RFID-based sensing system, which significantly improves the tracking accuracy, enabling mobile object tracking and even material recognition. We design and implement our prototype on commodity RFID devices. Comprehensive experiments show that TagRay achieves a high accuracy of 1.3cm which is a 200% improvement over the-state-of-arts for trajectory tracking. For commonly seen four material types, the material recognition accuracy is higher than 95% even with interference from people moving around.