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

arXiv: https://arxiv.org/abs/2505.08088 · HTML: https://mlsanity.com/paper/2505.08088/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 52 references that are in the map.
- First posted on arXiv 12 May 2025.

## Builds on (most cited first, 9 of 9)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [A Scalable Deep Neural Network Architecture for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi Fingerprinting](https://mlsanity.com/paper/1712.01990/) (2017)
- [Hierarchical Multi-Building And Multi-Floor Indoor Localization Based On Recurrent Neural Networks](https://mlsanity.com/paper/2112.12478/) (2021)
- [Learning Stance Embeddings from Signed Social Graphs](https://mlsanity.com/paper/2201.11675/) (2022)
- [Exploring the limits of community detection strategies in complex networks](https://mlsanity.com/paper/1306.4149/) (2013)
- [Node-Centric Detection of Overlapping Communities in Social Networks](https://mlsanity.com/paper/1607.01683/) (2016)
- [Hierarchical Stage-Wise Training of Linked Deep Neural Networks for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi RSSI Fingerprinting](https://mlsanity.com/paper/2407.13288/) (2024)
- [WiFi Based Distance Estimation Using Supervised Machine Learning](https://mlsanity.com/paper/2208.07190/) (2022)
- [Selecting a suitable Parallel Label-propagation based algorithm for Disjoint Community Detection](https://mlsanity.com/paper/2301.09125/) (2023)

## Abstract (arXiv)

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.