While gaining significant attention for device-free applications, Wi-Fi sensing still faces challenges in differentiating multiple targets; this stems from the design priorities of Wi-Fi systems that prioritize coverage and stability over sensing diversity. Existing proposals that either expand bandwidth or increase antennas to enhance sensing diversity can be confined by the limited access to Wi-Fi firm/hardware. To this end, we propose Mirror-Fi, a novel Wi-Fi sensing system that improves sensing diversity without modifying Wi-Fi firm/hardware. Exploiting the reconfigurability of metasurfaces, Mirror-Fi augments beamforming with spatially significant features, facilitating the construction of exclusive sensing signal links for individual targets. We innovate in an encoding scheme that equips each metasurface with a distinct phase coding sequence to mark link uniqueness. We then train a deep neural model to leverage prior coding sequences for decomposing non-linearly superimposed channel samples into mutually independent channels; it removes the need for complex channel matrix parameter estimation and mitigates hardware-related offsets inherent to Wi-Fi. Extensive evaluations demonstrate that, with a sufficient number of auto-configured metasurfaces, Mirror-Fi successfully achieves multi-target sensing.

With global population growth, sustainable agriculture requires efficient soil moisture sensing for precise irrigation. While commercial soil moisture sensors are often limited by cost and durability, state-of-the-art RF-based sensing solutions require additional signal transmitter infrastructure, hindering widespread adoption. To fill this gap, we introduce GPSoil—a novel soil moisture sensing system that leverages pervasive Global Navigation Satellite System (GNSS) signals. On the hardware side, we employ two antennas along with a low-cost RF switch, enabling error mitigation for long propagation distances by comparing the signals captured from both antennas. On the software side, we leverage the inherent clock drift errors in commercial GNSS sensors and turn them into tools for fine-grained value extraction, enabling high-resolution sensing from otherwise coarse data. By integrating hardware and software innovations, we successfully achieve practical soil moisture sensing using GNSS signals. Built with off-the-shelf components, GPSoil achieves a soil moisture accuracy of 5.2% at a material cost of only $10.56, making it far more affordable than existing systems. GPSoil can sense moisture up to one meter underground, far surpassing other RF-based sensing systems and meeting the needs of most crops and irrigation systems. This work pioneers the use of GNSS signals in agriculture, offering a scalable, low-cost solution for advancing precision irrigation and promoting sustainable agriculture.