Chenren Xu

Whose move is it anyway? Authenticating smart wearable devices using unique head movement patterns

In this paper, we present the design, implementation and evaluation of a user authentication system, Headbanger, for smart head-worn devices, through monitoring the users unique head-movement patterns in response to an external audio stimulus. Compared to todays solutions, which primarily rely on indirect authentication mechanisms via the users smartphone, thus cumbersome and susceptible to adversary intrusions, the proposed head-movement based authentication provides an accurate, robust, light-weight and convenient solution. Through extensive experimental evaluation with 95 participants, we show that our mechanism can accurately authenticate users with an average true acceptance rate of 95.57% while keeping the average false acceptance rate of 4.43%. We also show that even simple head-movement patterns are robust against imitation attacks. Finally, we demonstrate our authentication algorithm is rather light-weight: the overall processing latency on Google Glass is around 1.9 seconds.

The Case for Efficient and Robust RF-Based Device-Free Localization

Radio frequency based device-free localization has been proposed as an alternative localization technique. Unlike its active localization counterpart, it does not require subjects to wear any radio device, but tries to determine the subjects location by observing how much the subject disturbs the radio propagation patterns. This problem is very challenging due to the well known multipath effect, especially in a complex indoor environment where it is impractical to accurately model the effects of a subject on the surrounding radio links. In this article, we formulate the device-free localization problem using probabilistic classification approaches that are based on discriminant analysis.To boost the localization accuracies, we adopt methods to mitigate errors caused by the multipath effect, as well as methods to automatically recalibrate training data so that accuracy can be maintained as the environment evolves. We validate our method in a one-bedroom apartment that consists of 32 cells, using eight fixed transmitters and eight fixed receivers. When the space has a single occupant, our method can correctly estimate the occupied cell with a likelihood as high as 97.2 percent. Further, we show that we can maintain a high localization accuracy, while substantially reducing the deployment overhead, which is an important concern for device-free localization methods. To achieve this goal, we have improved our training and testing procedures to reduce the overhead, studied the radio device placement to optimize the device cost, devised algorithms to extend the lifetime of the training data, and designed a set of auxiliary sensors and incorporate them into the system to achieve automatic re-calibration.

Monitoring a Person's Heart Rate and Respiratory Rate on a Shared Bed Using Geophones

Using geophones to sense bed vibrations caused by ballistic force has shown great potential in monitoring a persons heart rate during sleep. It does not require a special mattress or sheets, and the user is free to move around and change position during sleep. Earlier work has studied how to process the geophone signal to detect heartbeats when a single subject occupies the entire bed. In this study, we develop a system called VitalMon, aiming to monitor a persons respiratory rate as well as heart rate, even when she is sharing a bed with another person. In such situations, the vibrations from both persons are mixed together. VitalMon first separates the two heartbeat signals, and then distinguishes the respiration signal from the heartbeat signal for each person. Our heartbeat separation algorithm relies on the spatial difference between two signal sources with respect to each vibration sensor, and our respiration extraction algorithm deciphers the breathing rate embedded in amplitude fluctuation of the heartbeat signal. We have developed a prototype bed to evaluate the proposed algorithms. A total of 86 subjects participated in our study, and we collected 5084 geophone samples, totaling 56 hours of data. We show that our technique is accurate -- its breathing rate estimation error for a single person is 0.38 breaths per minute (median error is 0.22 breaths per minute), heart rate estimation error when two persons share a bed is 1.90 beats per minute (median error is 0.72 beats per minute), and breathing rate estimation error when two persons share a bed is 2.62 breaths per minute (median error is 1.95 breaths per minute). By varying sleeping posture and mattress type, we show that our system can work in many different scenarios.

Transmit Only: An Ultra Low Overhead MAC Protocol for Dense Wireless Systems

The number of small wireless devices is rapidly increasing, making the radio channel efficiency in limited geographic areas (individual rooms or buildings) an important metric for MAC protocols. Many of these emerging devices have use-cases that are difficult to satisfy with current hardware solutions and channel access methods; for instance device mobility, small energy reserves, and requirements for low cost and small form factors. However, for most of these applications, such as health care monitoring or sensing, feedback to the radio device is unnecessary and unidirectional communication techniques are not only sufficient, but can also be advantageous. We propose an efficient, reliable technique for unidirectional communication, called Transmit Only (TO), that satisfies these requirements while maintaining packet throughput guarantees and reducing energy consumption. In this paper we will demonstrate the feasibility and performance of this kind of highly asymmetric, transmit-only protocol through theoretical, simulated, and experimental results.

Demo of Headbanger: Authenticating smart wearable devices using unique head movement patterns

We demonstrate a system for direct authentication of users to their head-worn wearable device through a novel approach that identifies users based on motion signatures extracted from their head-movements. This approach is in contrast to existing indirect authentication solutions via smartphone or using touch-pad swipe patterns. The system, dubbed Headbanger, is a software authentication solution that leverages unique motion patterns created when users shake their head in response to music played on the head-worn device, and sensed through integrated accelerometers. In this demo, we demonstrate Headbanger on Google Glass and show the effectiveness of the system in two authentication modes, that include (i) a trained user reliably authenticated to the owned Glass device, and (ii) an attacker being prevented from login when attempting to login to the Glass device by imitating the owners head-movements.

What Am I Looking At? Low-Power Radio-Optical Beacons for In-View Recognition on Smart-Glass

Applications on wearable personal imaging devices, or Smart-glasses as they are called, can largely benefit from accurate and energy-efficient recognition of objects that are within the users view. Existing solutions such as optical or computer vision approaches are too energy intensive, while low-power active radio tags suffer from imprecise orientation estimates. To address this challenge, this paper presents the design, implementation, and evaluation of a radio-optical hybrid system where a radio-optical transmitter, or tag, whose radio-optical beacons are used for accurate relative orientation tracking of tagged objects by a wearable radio-optical receiver. A low-power radio link that conveys identity is used to reduce the battery drain by synchronizing the radio-optical transmitter and receiver so that extremely short optical (infrared) pulses are sufficient for orientation (angle and distance) estimation. Through extensive experiments with our prototype we show that our system can achieve orientation estimates with 1-to-2 degree accuracy and within 40 cm ranging error, with a maximum range of 9 m in typical indoor use cases. With a tag and receiver battery power consumption of 81 μW and 90 mW, respectively, our radio-optical tags and receiver are at least 1.5x energy efficient than prior works in this space.

Software-defined Visible Light Backscatter Network

We introduce PassiveVLN, a flexible, modular and software-defined platform for visible light backscatter networks. PassiveVLN incorporates a modular hardware design and a full-stack software implementation, enabling convenient and scalable deployment as well as rapid prototyping for testing new protocols and applications.

Long Range Retroreflective V2X Communication with Polarization-based Differential Reception

Vehicle-to-anything (V2X) communications technology is an essential substrate to realize future road intelligence and autonomous driving, especially in the areas where there are no existing (radio) network infrastructure. The emerging visible light backscatter communication technique shows great potentials in enabling the massive on-road retroreflective objects to delivery dynamic information to host vehicles. In this work, we design a polarization-based differential reception scheme to suppress ambient noise and realize long range retroreflective V2X communications.

CoSDEO 2016: Contact-free ambient sensing - Welcome and committees: Welcome message from the CoSDEO 2016 workshop co-chairs

It is our great pleasure to welcome you to the Fifth CoSDEO Workshop in conjunction with the IEEE Intemational Conference on Pe1vasive Computing and Communications (PerCom 2016). This is the first time that the CoSDEO comes to Australia and the first time that it is co-located with PerCom. This years edition focuses on contact-free ambient sensing, localisation and tracking, extending the successful theme of the fourth CoSDEO workshop on Device-Free Radio-Based Recognition. This years program has selected 6 exciting papers embracing a number of diverse perspectives across the field. Regarding the submission and review process for CoSDEO 2016, each paper was reviewed by at least two to five technical program committee (TPC) members. This year, 6 excellent papers were finally selected for inclusion at the workshop. The topics of the papers reflect the wide spectrum research around mobile computing, se1vices, and applications, ranging from activity recognition, sentiment sensing, advances towards accurate Radio-Tomographic Imaging, Deep-Leaming approaches to RFbased recognition, Audio-based mechanisms, implementations utilising depth-sensors as well as multivariate ambient sensing.

SenSys'15 Proceedings Workshop Summary Abstract / IoT-App'15: The 2015 International Workshop on Internet of Things towards Applications

After a very successful edition of the IoT-App workshop, its second edition is conducted in conjunction with SenSys 2015. Again, the workshop succeeded in attracting a high number of high-quality submissions. The topics of the papers submitted feature most prominently urban sensing and smart home or city. Further directions are programming concepts for IoT devices as well as advances in machine learning, particularly deep learning for IoT devices.