Scalable and Stable WiFi Localization (SSWLoc): Through on-Demand Map Extraction and Adaptive Similarity Weighting Based on Signal Strength

Wi-Fi indoor positioning systems (IPS) often face stability and scalability problems with traditional mapping algorithms such as K-nearest neighbors and cosine similarity. These methods rely on vector maps, leading to high processing overhead and scalability challenges in large testbeds owing to the sparse vector. Additionally, these algorithms fail to consider signal strength and physical coordinates, causing long-term stability problems. These problems can lead to performance degradation during the long-term deployment of Wi-Fi IPS. To address scalability, we dynamically extracted local map data from raw radio frequency (RF) maps using scanned service set identifiers (SSIDs). For stability, we propose an adaptive weighted sum similarity mapping algorithm that uses a scaling factor per received signal strength (RSS). We deployed our Wi-Fi IPS in a 219,200 m² COEX mall, a major indoor complex in South Korea. While the traditional vector map required 200.42 MB of data, our method only needed 41.62 KB, reducing the load size by 99.98%. In five test scenarios, traditional algorithms had average errors of 7.14 m and 8.02 m, whereas our algorithm reduced the error to 2.78 m. After six months, traditional methods' errors increased to 16.04 m and 17.11 m, but our method maintained an average error of 5.92 m.