Willow: Practical WiFi Backscatter Localization with Parallel Tags

WiFi backscatter localization is a promising technology for the Internet of Things. However, existing works cannot work well for large-scale and low-cost tags with commodity WiFi devices. We present Willow, which provides accurate localization for parallel backscatter tags with commodity WiFi devices. We design a packet-level orthogonal backscatter modulation method to generate multiple orthogonal backscatter signals and support in-band backscatter with ambient WiFi. We show that backscatter signals can be effectively extracted even under strong in-band interference. To work in real WiFi traffic, we propose adaptive packet selection-based modulation to guarantee the orthogonality of backscatter signals. For parallel localization, we propose an iterative inter-tag interference cancellation method and a location filtering method to remove location ambiguity. We theoretically analyze the effectiveness of our method in supporting parallel tags. We prototype Willow tags using low-cost hardware and implement Willow AP on commodity WiFi NIC AX200. Through extensive experiments, we show that Willow achieves a median localization error of 27 cm and supports 51 parallel tags, which is 2× and 17× better than the state-of-the-art method.