A Robust Multifloor Wi-Fi Fingerprinting Approach Based on Geospatial Cells for Indoor Localization

Wi-Fi fingerprinting uses wireless signals to locate smartphone users indoors effectively. This method is popular because many buildings have numerous wireless access points (APs) available. However, due to the complex multifloor indoor environment with its inherent signal dynamics, it suffers from: 1) computationally inefficient and complex algorithms designed to overcome the spatial unevenness and feature sparsity of the dataset; 2) high sensitivity to environmental dynamics; and 3) low accuracies due to signal fluctuations and inconsistencies between the physical and signal domains. To address these issues, we formulated a robust Wi-Fi fingerprinting approach for multifloor indoor localization that accurately finds the user’s residing floor and location coordinates. The computational complexities are reduced by organizing the reference points (RPs) into geospatial cells (G-cells), extracting useful statistical features, and finding the localized cells (LCs) leveraging the knowledge of common APs with the test point (CATP) and the nearest RP (N-RP). The algorithm removes outlier N-RPs, and the localized KNN algorithm (L-KNN) finds the real-time user’s coordinates accurately and efficiently. The floor identification algorithm, comprising two modules, segregates LCs into a layered format and utilizes information from neighboring floors’ APs common to the test point (TP) to overcome spatial unevenness between training and test samples. Extensive experiments are conducted on three real-world datasets. The results demonstrate that the proposed approach has superior localization accuracy and robustness compared to the best methods in the literature.