CrossSense: Enabling Cross-Technology Sensing between WiFi and LoRa

With the explosive increase in wireless devices, enabling sensing between incompatible radios has become critically beneficial. Integrating diverse IoT devices enhances sensing accuracy by providing richer data, while utilizing the diverse characteristics of heterogeneous signals meets sensing needs in complex environments. However, most existing wireless sensing methods primarily focus on homogeneous signals, while research on sensing with heterogeneous signals is still in its infancy. In this paper, we propose CrossSense, a novel Cross-Technology Sensing (CTS) framework that enables sensing between incompatible WiFi and LoRa device. CrossSense recovers the fine-grained trajectory of a WiFi transmitter based on its emulated LoRa signals. To decompose the motion feature components of WiFi transmitter, we develop a chirp difference vector model that utilizes the energy peak within each chirp window for sensing. We model the relationship between sampling frequency offsets and oscillation frequency offsets among heterogeneous devices to guide the extraction of motion features from the emulated signal. We also propose a greedy-based peak enhancement method to calculate the optimized LoRa phases, minimizing the impact of phase discontinuity caused by cyclic prefix (CP) errors. We implement a prototype of CrossSense on the USRP platform. The extensive experiments demonstrate that CrossSense can achieve an efficient Cross-Technology Sensing with $2.92cm$ distance accuracy and $0.26cm/s$ speed accuracy over a $120m$ sensing range.