SDR Receiver Using Commodity WiFi via Physical-Layer Signal Reconstruction
With the explosive increase in wireless devices, physical-layer signal analysis has become critically beneficial across distinctive domains including interference minimization in network planning, security and privacy (e.g., drone and spycam detection), and mobile health with remote sensing. While SDR is known to be highly effective in realizing such services, they are rarely deployed or used by the end-users due to the costly hardware ~1K USD (e.g., USRP). Low-cost SDRs (e.g., RTL-SDR) are available, but their bandwidth is limited to 2-3 MHz and operation range falls well below 2.4 GHz ? the unlicensed band holding majority of the wireless devices. This paper presents SDR-Lite, the first zero-cost, software-only soft-ware defined radio (SDR) receiver that empowers commodity WiFi to retrieve the In-phase and Quadrature of an ambient signal. With the full compatibility to pervasively-deployed WiFi infrastructure (without any change to the hardware and firmware), SDR-Lite aims to spread the blessing of SDR receiver functionalities to billions of WiFi users and households to enhance our everyday lives. The key idea of SDR-Lite is to trick WiFi to begin packet reception (i.e., the decoding process) when the packet is absent, so that it accepts ambient signals in the air and outputs corresponding bits. The bits are then reconstructed to the original physical-layer waveform, on which diverse SDR applications are performed. Our comprehensive evaluation shows that the reconstructed signal closely reassembles the original ambient signal (>85% correlation). We extensively demonstrate SDR-Lite effectiveness across seven distinctive SDR receiver applications under three representative categories: (i) RFfingerprinting, (ii) spectrum monitoring, and (iii) (ZigBee) decoding. For instance, in security applications of drone and rogue WiFi AP detection, SDR-Lite achieves 99% and 97% accuracy, which is comparable to USRP.
NEMO: Enabling Neural-enhanced Video Streaming on Commodity Mobile Devices
The demand for mobile video streaming has experienced tremendous growth over the last decade.
However, existing methods of video delivery fall short of delivering high-quality video.
Recent advances in neural super-resolution have opened up the possibility of enhancing video quality by leveraging client-side computation.
Unfortunately, mobile devices cannot benefit from this because it is too expensive in computation and power-hungry.
To overcome the limitation, we presentNEMO, a system that enables real-time video super-resolution on mobile devices.
NEMO applies neural super-resolution to a few select frames and transfers the outputs to benefit the remaining frames.
The frames to which super-resolution is applied are carefully chosen to maximize theoverall quality gains.
NEMO leverages fine-grained dependencies using information from the video codec and strives to provide guarantees in the quality degradation compared to per-frame super-resolution.
Our evaluation using a full system implementation on Android shows NEMO improves the overall processing throughputby x11.5, reduces energy consumption by 88.6%, and maintains device temperatures at acceptable levels compared to per-frame super-resolution, while ensuring high video quality.
Overall, this leads to a 31.2% improvement in quality of experience for mobile users.
Lumos: Improving Smart Home IoT Visibility and Interoperability Through Analyzing Mobile Apps
The era of Smart Homes and the Internet of Things (IoT) calls for integrating diverse "smart" devices, including sensors, actuators, and home appliances. However, enabling interoperation across heterogeneous IoT devices is a challenging task because vendors use their own control and communication protocols. Prior approaches have attempted to solve this problem by asking for vendor support, or even fundamentally re-designing the architecture of IoT devices. These approaches face limitations as they require disruptive changes. This paper explores a new approach to improving IoT interoperability without requiring architectural changes or vendor participation. Focusing on smart-home environments, we propose Lumos that improves interoperability by leveraging Android apps that control IoT devices. Lumos uses this information learned from IoT apps to enable "best-effort" interoperation across heterogeneous devices. Our evaluation with 15 commercial IoT devices from three major IoT platforms and in-depth user studies conducted with 24 participants demonstrate the promising efficacy of Lumos for implementing diverse interoperation scenarios.