LCWF: Low-Overhead Collaborative WiFi Fingerprint Localization Considering Devices Heterogeneity

Ye Tao, Shaolin Tan, Rongen Yan, Nian Liu, Qing Gao, Jinhu Lü

Abstract: Traditional WiFi fingerprint based localization scheme relies on high-overhead signal collection, and then estimates the location for each user, respectively. Is it feasible to achieve a higher estimate accuracy using a low-overhead fingerprint approach, maximizing the utilization of collaborative information shared among users? This paper provides an affirmative answer. Specifically, we propose an emerging collaborative localization scheme named LCWF, which first uses access point (AP) rank as a feature to discretize and cluster the area of interest. Then, we propose a signal strength difference (SSD) similarity metric, to discern which clustering area a user belongs to and select users with robust signal relationships. Finally, the received signal strength (RSS) relationships between selected users are leveraged to infer their corresponding physical location relationships, for outputting the user final location. Furthermore, considering the device heterogeneity among either mobile devices or APs, we propose a device calibration algorithm to standardize the collaborative localization process among heterogeneous online devices. Real-world experiments results validate the effectiveness of our proposed LCWF system, compared to other state-of-the-art approaches (e.g. NN, OCLoc, CBWF+). Specifically, in a 40 m × 17 m real scenario with only 20 reference points (RPs) and 11 access points (APs), our algorithm achieves an average localization accuracy of 3.42 m for a heterogeneous dataset.

External IDs:doi:10.1109/tnse.2025.3621235