Graph-Based Floor Separation Using Node Embeddings and Clustering of WiFi Trajectories

• URL: http://arxiv.org/abs/2505.08088v2
• Date: Fri, 13 Jun 2025 15:48:03 GMT
• Title: Graph-Based Floor Separation Using Node Embeddings and Clustering of WiFi Trajectories
• Authors: Rabia Yasa Kostas, Kahraman Kostas,
• Abstract summary: This study proposes a novel graph-based approach for floor separation using Wi-Fi fingerprint trajectories.<n>We construct a graph where nodes represent Wi-Fi fingerprints, and edges are weighted by signal similarity and contextual transitions.<n>The proposed approach demonstrates robustness to signal noise and architectural complexities, offering a scalable solution for floor-level localization.
• Score: 1.1510009152620668
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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