Wireless communications are ubiquitous nowadays, especially in the new era of Internet of Things (IoT). Most of IoT devices access the Internet via some kind of wireless connections. The major role of wireless signals is a type of communication medium. Besides that, taking advantage of the growing physical layer capabilities of wireless techniques, recent research has demonstrated the possibility of reusing wireless signals for both communication and sensing. The capability of wireless sensing and the ubiquitous availability of wireless signals make it possible to meet the rising demand of pervasive environment perception. Physical layer features including signal attributes and channel state information (CSI) can be used for the purpose of physical world sensing. This dissertation focuses on exploring the sensing capability of wireless signals. The research approach is to first take measurements from physical layer of wireless connections, and then develop various techniques to extract or infer information about the environment from the measurements, like the locations of signal sources, the motion of human body, etc.

The research work in this dissertation makes three contributions. We start from wireless signal attributes analysis. Specifically, the cyclostationarity properties of wireless signals are studied. Taking WiFi signals as an example, we propose signal cyclostationarity models induced by WiFi Orthogonal Frequency Division Multiplexing (OFDM) structure including pilots, cyclic prefix, and preambles. The induced cyclic frequencies is then applied to the signal-selective direction estimation problem.

Second, based on the analysis of wireless signal attributes, we design and implement a prototype of a single device system, named MobTrack, which can locate indoor interfering radios. The goal of designing MobTrack is to provide a lightweight, handhold system that can locate interfering radios with sub-meter accuracy with as few antennas as possible. With a small antenna array, the cost, complexity as well as size of this device are reduced. MobTrack is the first single device indoor interference localization system without the requirement of multiple pre-deployed access points (AP).

Third, channel state information is studied in applications of human motion sensing. We design WiTalk, the first system which is able to do fine-grained motion sensing like leap reading on smartphones using the CSI dynamics generated by human movements. WiTalk proposes a new fine-grained human motion sensing technique with the distinct context-free feature. To achieve this goal using CSI, WiTalk generates CSI spectrograms using signal processing techniques and extracts features by calculating the contours of the CSI spectrograms. The proposed technique is verified in the application scenario of lip reading, where the fine-grained motion is the mouth movements.