Hongbo Liu

TL;DR: This paper presents device-free location-oriented activity identification at home through the use of existing WiFi access points and WiFi devices (e.g., desktops, thermostats, refrigerators, smartTVs, laptops) in a low-cost system that can uniquely identify both in-place activities and walking movements across a home by comparing them against signal profiles.

TL;DR: A peer assisted localization approach is proposed that can reduce the maximum and 80-percentile errors to as small as $2m$ and $1m, in time no longer than the original WiFi scanning, with negligible impact on battery lifetime.

TL;DR: This work addresses the fundamental problem of distinguishing between a driver and passenger using a mobile phone, which is the critical input to enable numerous safety and interface enhancements, and leverages the existing car stereo infrastructure, in particular the speakers and Bluetooth network.

TL;DR: This paper shows that exploiting the channel response from multiple Orthogonal Frequency-Division Multiplexing (OFDM) subcarriers can provide fine-grained channel information and achieve higher bit generation rate for both static and mobile cases in real-world scenarios, and develops a Channel Gain Complement (CGC) assisted secret key extraction scheme to cope with channel non-reciprocity encountered in practice.

TL;DR: This paper supports the extended concept of user authentication with a device-free approach by leveraging the prevalent WiFi signals made available by IoT devices, such as smart refrigerator, smart TV and thermostat, etc.