freeLoc: Wireless-Based Cross-Domain Device-Free Fingerprints Localization to Free User’s Motions

Due to contactless and convenient experiences, WiFi-based device-free fingerprints localization technologies have extensively attracted research attention. However, they are studied based on an assumption that the user is stationary and face a major challenge in the presence of users’ motions. That is, because users’ motions induced CSI WiFi variations results in inconsistent location fingerprints during training and prediction, leading to system ineffective. To solve this problem, in this article, we propose freeLoc, which aims to free users’ motions (even unseen) while maintaining accurate localization. Specifically, we construct a domain adaptation network that defines different users and motions as different domains, and learns domain-independent representations to extract location fingerprints independent of users’ motions. Unfortunately, collecting sufficient amounts of WiFi data is difficult. To reduce the cost of labeling data and ensure the performance of domain adaptation network, we utilize adversarial autoencoder to build a data augmentation module to introduce data diversity. We deploy experiments in a real scenario, and the results show that only by labeling three motions of three users, we can achieve accurate localization (the nearest locations are about one meter away) for a total of 36 domains, including 6 users and 6 motions. Compared to other existing technologies, freeLoc can improve location prediction accuracy by up to 35%.