In this paper, we address a critical task of dynamically detecting the simultaneous behavior of driving and texting using smartphone as the sensor. We propose, design, and implement TEXIVE which achieves the goal of detecting texting operations during driving utilizing irregularities and rich micro-movements of users. Without relying on any external infrastructures and additional devices, and no need to bring any modification to vehicles, TEXIVE is able to successfully detect dangerous operations with good sensitivity, specificity, and accuracy by leveraging the inertial sensors integrated in regular smartphones. To validate our approach, we conduct extensive experiments involving in a number of volunteers on various of vehicles and smartphones. Our evaluation results show that TEXIVE has a classification accuracy of 87.18%, and precision of 96.67%.

Visible light communication (VLC) has been shown to have several advantages over traditional wireless communication. In this paper, we envision an LED-light-to-smartphone VLC protocol for delivering messages to a group of randomly arriving smartphone receivers. Our goal is to increase the throughput for large message delivery, as well as to reduce the delay of message broadcast. Key challenges for implementing such a VLC message broadcast protocol are: 1) the imperfect synchronization among receivers and the transmitter; 2) the receivers' arbitrary arrival times; and 3) the diversity of receivers' smartphones (e.g., location, capability, and frame-rates). In this paper, we propose a new modulation scheme and design link-layer protocols for improving the network data rate. We carefully design and implement our protocol, Martian, which allows smooth communication from the LED lights to a group of smartphone embedded cameras. Across several phone models, Martian can achieve data rate of about 1.6 kb/s even with NLOS -light. It also has a stable and small delay for broadcasting messages to the randomly arriving receivers.

Visible light communication (VLC) has been shown to have several advantages over traditional wireless communication. We envision a LED-to-smartphone VLC protocol for delivering messages to a group of unsynchronized mobile device receivers. We carefully design and implement our protocol, Martian, which allows smooth communication from the LED lights to a group of camera-enabled mobile devices. Across several phone models, Martian can achieve data rate of about 1.6kbps even with NLOS-light. Our intensive evaluations indicate that, the data rate reaches 4.2kbps on iPhone 6. This is a significant improvement compared with the 88bps data rate claimed by state-of-art design.

This paper presents a framework for privacy-preserving video delivery system to fulfill users' privacy demands. The proposed framework leverages the inference channels in sensitive behavior prediction and object tracking in a video surveillance system for the sequence privacy protection. For such a goal, we need to capture different pieces of evidence which are used to infer the identity. The temporal, spatial and context features are extracted from the surveillance video as the observations to perceive the privacy demands and their correlations. Taking advantage of quantifying various evidence and utility, we let users subscribe videos with a viewer-dependent pattern. We implement a prototype system for off-line and on-line requirements in two typical monitoring scenarios to construct extensive experiments. The evaluation results show that our system can efficiently satisfy users' privacy demands while saving over 25% more video information compared to traditional video privacy protection schemes.

In this work, we explore a new networking mechanism with smart glasses, through which users can express their interest and connect to a target simply by a gaze. Doing this, we attempt to let wearable devices understand human attention and intention, and pair devices carried by users according to such attention and intention. To achieve this ambitious goal, we propose a proof-of-concept system iGaze, a visual attention driven networking suite: an iGaze glass (hardware), and a networking protocol VAN (software). Our glass, iGaze glass, is a low-cost head-mounted glass with a camera, orientation sensors, microphone and speakers, which are embedded with our software for visual attention capture and networking. A visual attention driven networking protocol (VAN) is carefully designed and implemented. In VAN, we design an energy efficient and highly accurate visual attention determination scheme using single camera to capture user's communication interest and a double-matching scheme based on visual direction detection and Doppler effect of acoustic signal to lock the target devices. Using our system, we conduct a series of trials for various application scenarios to demonstrate the effectiveness of our system.

In this demo, we propose a proof-of-concept networking system for smart glasses, through which users can express their interest and connect to a target simply by a gaze. Our system iGaze is a visual attention driven networking suite: an iGaze glass (hardware) and a networking protocol VAN (software). Our glass is a low-cost head-mounted glass with a camera, orientation sensors, microphone and speakers, which are embedded with our software for visual attention capture and networking. A visual attention driven networking protocol (VAN) is carefully designed and implemented. In VAN, we design an energy efficient and highly accurate visual attention determination scheme using single camera to capture user's communication interest and a double-matching scheme based on visual direction detection and Doppler effect of acoustic signal to lock the target devices. iGaze has separated and modularized hardware and software design. It can run on top of existing networking protocols, e.g., Wi-Fi.

In this work, we address a critical task of detecting the user behavior of driving and texting simultaneously using smartphones. We propose, design, and implement TEXIVE which achieves the goal of distinguishing drivers and passengers, and detecting texting operations during driving utilizing irregularities and rich micro-movements of users. Without relying on any external infrastructures and additional devices, and no need to bring any modification to vehicles, TEXIVE is able to successfully detect dangerous operations with good sensitivity, specificity and accuracy. We conduct experimental study of TEXIVE with the help of a number of volunteers using various vehicles and smartphones. Our results indicate that TEXIVE has a classification accuracy of 87.18%, and precision of 96.67%.