Internet-of-things (IoT) devices are becoming widely adopted, but they increasingly suffer from limited power, as power cords cannot reach the billions and batteries do not last forever. Existing systems address the issue with ultra-low-power designs and energy scavenging, which inevitably limit functionality. To unlock the full potential of ubiquitous computing and connectivity, our solution uses capacitive power transfer (CPT) to provide battery-like wireless power delivery, henceforth referred to as "Capttery". Capttery presents the first room-level (~5 m) CPT system, which delivers continuous milliwatt-level wireless power to multiple IoT devices concurrently. Unlike conventional one-to-one CPT systems that target kilowatt power in a controlled and potentially hazardous setup, Capttery is designed to be human-safe and invariant in a practical and dynamic environment. Our evaluation shows that Capttery can power end-to-end IoT applications across a typical room, where new receivers can be easily added in a plug-and-play manner.

Owing to dense deployment of light fixtures and multipath-free propagation, visible light localization technology holds potential to overcome the reliability issue of radio localization. However, existing visible light localization systems require customized light hardware, which increases deployment cost and hinders near-term adoption. In this paper, we propose LiTell, a simple and robust localization scheme that employs unmodified fluorescent lights (FLs) as location landmarks and commodity smartphones as light sensors. LiTell builds on the key observation that each FL has an inherent characteristic frequency which can serve as a discriminative feature. It incorporates a set of sampling, signal amplification, and camera optimization mechanisms, that enable a smartphone to capture the extremely weak and high-frequency (greater than 80 kHz) features. We have implemented LiTell as a real-time localization and navigation system on Android. Our experiments demonstrate LiTell's high reliability in discriminating different FLs, and its potential to achieve sub-meter location granularity. Our user study in a multi-story office building, parking lot, and grocery store further validates LiTell as an accurate, robust, and ready-to-use indoor localization system.

The past decade's research in visible light positioning (VLP) has led to technologies with high location precision. However, existing VLP systems either require specialized LEDs which hinder large-scale deployment, or need cameras which preclude continuous localization because of high power consumption and short coverage. In this paper, we propose Pulsar, which uses a compact photodiode sensor, readily fit into a mobile device, to discriminate existing ceiling lights---either fluorescent lights or conventional LEDs---based on their intrinsic optical emission features. To overcome the photodiode's lack of spatial resolution, we design a novel sparse photogrammetry mechanism, which resolves the light source's angle-of-arrival, and triangulates the device's 3D location and even orientation. To facilitate ubiquitous deployment, we further develop a light registration mechanism that automatically registers the ceiling lights' locations as landmarks on a building's floor map. Our experiments demonstrate that Pulsar can reliably achieve decimeter precision in both controlled environment and large-scale buildings.

The ubiquity of mobile camera devices has been triggering an outcry of privacy concerns, whereas privacy protection still relies on the cooperation of the photographer or camera hardware, which can hardly be guaranteed in practice. In this paper, we introduce LiShield, which automatically protects a physical scene against photographing, by illuminating it with smart LEDs flickering in specialized waveforms. We use a model-driven approach to optimize the waveform, so as to ensure protection against the (uncontrollable) cameras and potential image-processing based attacks. We have also designed mechanisms to unblock authorized cameras and enable graceful degradation under strong ambient light interference. Our prototype implementation and experiments show that LiShield can effectively destroy unauthorized capturing while maintaining robustness against potential attacks.

The ubiquity of mobile camera devices has been triggering an outcry of privacy concerns, whereas privacy protection still relies on the compliance of the photographer or camera hardware, which can hardly be guaranteed in practice. In this demo, we introduce LiShield, which automatically protects a physical scene against photographing, by illuminating it with smart LEDs flickering in a specialized waveform. We use a model-driven approach to optimize the waveform, so as to ensure protection against the (uncontrollable) cameras and potential image-processing based attacks. We have also designed mechanisms to unblock authorized cameras and enable graceful degradation under strong ambient light interference. This demo will show our prototype implementation, with simple on-site experiments that demonstrate how LiShield effectively destroys unauthorized photo capturing.

Owing to dense deployment of light fixtures and multipath-free propagation, visible light localization technology holds potential to overcome the reliability issue of radio localization. However, existing visible light localization systems require customized light hardware, which increases deployment cost and hinders near term adoption. In this paper, we propose LiTell, a simple and robust localization scheme that employs unmodified fluorescent lights (FLs) as location landmarks and commodity smartphones as light sensors. LiTell builds on the key observation that each FL has an inherent characteristic frequency which can serve as a discriminative feature. It incorporates a set of sampling, signal amplification and camera optimization mechanisms, that enable a smartphone to capture the extremely weak and high frequency ( > 80 kHz) features. We have implemented LiTell as a real-time localization and navigation system on Android. Our experiments demonstrate LiTell's high reliability in discriminating different FLs, and its potential to achieve sub-meter location granularity. Our user study in a multi-storey office building, parking lot and grocery store further validates LiTell as an accurate, robust and ready-to-use indoor localization system.

Owing to dense deployment of light fixtures and multipath-free propagation, visible light localization technology holds potential to overcome the reliability issue of radio localization. However, existing visible light localization systems require customized light hardware, which increases deployment cost and hinders near term adoption. We present LiTell, a simple and robust localization scheme that employs unmodified fluorescent lights (FLs) as location landmarks and commodity smartphones as light sensors. LiTell builds on the key observation that each FL has an inherent characteristic frequency, which can serve as a discriminative feature. It incorporates a set of sampling, signal amplification and camera optimization mechanisms, that enable a smartphone to capture the extremely weak and high frequency (> 80 kHz) features. We have implemented LiTell as a real-time localization and navigation system on Android. In our experiments, LiTell demonstrates high reliability in discriminating different FLs, and great potential to achieve sub-meter granularity.

Visible light indoor localization offers promising accuracy and reliability due to dense light deployment and multipath-free propagation. However, current visible light localization schemes rely on either energy-hungry cameras or assumptions about channel responses of light sources, which limits their application. They also require LED lights with specialized circuitry, which increases cost and hinders large scale deployment. We present LiTell2, a scheme that extracts unique features from incumbent fluorescent or LED lights, and uses a simple photodiode sensor to extract AoA information for localization. LiTell2's sensor achieves AoA measurements by leveraging the diversity of photodiodes' angular responses. By comparing signal from photodiodes of different angular response, LiTell2 can derive AoA of each light source, thus achieving high precision localization with unmodified light fixtures and extremely low fingerprinting cost. Our experiments show that LiTell2 provides accurate AoA sensing and enables energy efficient yet robust localization.

Mobile devices are shrinking their form factors for portability, but user-mobile interaction is becoming increasingly challenging. In this paper, we propose a novel system called Okuli to meet this challenge. Okuli is a compact, low-cost system that can augment a mobile device and extend its interaction workspace to any nearby surface area. Okuli piggybacks on visible light communication modules, and uses a low-power LED and two light sensors to locate user's finger within the workspace. It is built on a light propagation/reflection model that achieves around one-centimeter location precision, with zero run-time training overhead. We have prototyped Okuli as an Android peripheral, with a 3D-printed shroud to host the LED and light sensors. Our experiments demonstrate Okuli's accuracy, stability, energy efficiency, as well as its potential in serving virtual keyboard and trackpad applications.

WiFi Display, also called Miracast, is an emerging technology that allows a mobile device (source) to duplicate its screen content to an external display (sink) via a peer-to-peer WiFi link. Despite its diverse application scenarios and growing popularity, Miracast consumes substantial power due to a combination of video encoding/decoding and transmission. In this paper, we first conduct a measurement study to quantify and model key parameters that scale Miracast's power consumption. We then propose a set of optimization mechanisms to bypass redundant codec operations, reduce video tail traffic, and relocate the Miracast channel dynamically to maximize transmission efficiency. We have implemented this energy-efficient Miracast framework on an Android smartphone. Experimental results show that the legacy Miracast system costs 1.3 to 2.4 Watts. Our framework reduces the power consumption by 29% to 61%, depending on the Miracast application's video traffic patterns. Our optimization mechanisms do not affect the video quality, and can even reduce the latency of certain Miracast applications.