CBWF+: Collaboratively Enhanced Lightweight Circular-Boundary-Based WiFi Fingerprinting

Users upload their own WiFi localization request signals to match against the received signal strengths (RSS) in an offline constructed fingerprint database, thereby obtaining their own geographic location estimates. However, the relationships between the measured signals of users are seldom explored for enhancing the localization accuracy. Furthermore, the tedious site surveys required for building an offline fingerprint database hinder the wider application of this technology. To address the above issues, this paper proposes a collaboratively enhanced lightweight WiFi localization algorithm named CBWF+, capable of providing an accurate indoor position with low-overhead fingerprints and collaborative localization. Specifically, we first discretize the area of interest to produce virtual points (VPs), and leverage the concept of circular-boundary-based localization to select a few VPs that share similar signal characteristics with the request signals. Then, important users for collaborative localization are chosen by two new proposed indicators. Next, based on the selection results and the RSS relationships among users, the spatiotemporal characteristics of WiFi signals are used to further filter out VPs that contradict the relationship between RSS and physical location. Finally, the credible VPs are used to obtain a high-accuracy estimate, by a proposed weighting method. The results of the real-world experiments validate the effectiveness of our proposed CBWF+ algorithm, compared to other state-of-the-art approaches (e.g. NN, OCLoc, CBWF, etc.). Specifically, in a 40 m × 17 m real scenario with only 20 reference points (RPs) and 11 access point (APs), our algorithm achieves an average localization accuracy of 2.49 m. Our codes are available at: https://github.com/dadadaray/CBWF2.0.