Graph-Based Floor Separation Using Node Embeddings and Clustering of WiFi Trajectories
This work addresses floor separation for indoor positioning systems, which is incremental as it builds on existing graph and clustering techniques with specific improvements.
The study tackled vertical localization in indoor positioning by proposing a graph-based method using Wi-Fi fingerprint trajectories, achieving an accuracy of 68.97%, an F1-score of 61.99%, and an Adjusted Rand Index of 57.19% on the Huawei University Challenge 2021 dataset.
Indoor positioning systems (IPSs) are increasingly vital for location-based services in complex multi-storey environments. This study proposes a novel graph-based approach for floor separation using Wi-Fi fingerprint trajectories, addressing the challenge of vertical localization in indoor settings. We construct a graph where nodes represent Wi-Fi fingerprints, and edges are weighted by signal similarity and contextual transitions. Node2Vec is employed to generate low-dimensional embeddings, which are subsequently clustered using K-means to identify distinct floors. Evaluated on the Huawei University Challenge 2021 dataset, our method outperforms traditional community detection algorithms, achieving an accuracy of 68.97\%, an F1-score of 61.99\%, and an Adjusted Rand Index of 57.19\%. By publicly releasing the preprocessed dataset and implementation code, this work contributes to advancing research in indoor positioning. The proposed approach demonstrates robustness to signal noise and architectural complexities, offering a scalable solution for floor-level localization.