# Hierarchical Stage-Wise Training of Linked Deep Neural Networks for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi RSSI Fingerprinting

arXiv: https://arxiv.org/abs/2407.13288 · HTML: https://mlsanity.com/paper/2407.13288/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 18 Jul 2024.

## Builds on (most cited first, 5 of 5)

- [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)
- [Low-effort place recognition with WiFi fingerprints using deep learning](https://mlsanity.com/paper/1611.02049/) (2016)
- [Hybrid Building/Floor Classification and Location Coordinates Regression Using A Single-Input and Multi-Output Deep Neural Network for Large-Scale Indoor Localization Based on Wi-Fi Fingerprinting](https://mlsanity.com/paper/1810.05868/) (2018)
- [Hierarchical Auxiliary Learning](https://mlsanity.com/paper/1906.00852/) (2019)

## Built on by (most cited first, 2 of 2)

- [Graph-Based Floor Separation Using Node Embeddings and Clustering of WiFi Trajectories](https://mlsanity.com/paper/2505.08088/) (2025)
- [From Global to Local: Cluster-Aware Learning for Wi-Fi Fingerprinting Indoor Localisation](https://mlsanity.com/paper/2601.05650/) (2026)

## Abstract (arXiv)

In this paper, we present a new solution to the problem of large-scale multi-building and multi-floor indoor localization based on linked neural networks, where each neural network is dedicated to a sub-problem and trained under a hierarchical stage-wise training framework. When the measured data from sensors have a hierarchical representation as in multi-building and multi-floor indoor localization, it is important to exploit the hierarchical nature in data processing to provide a scalable solution. In this regard, the hierarchical stage-wise training framework extends the original stage-wise training framework to the case of multiple linked networks by training a lower-hierarchy network based on the prior knowledge gained from the training of higher-hierarchy networks. The experimental results with the publicly-available UJIIndoorLoc multi-building and multi-floor Wi-Fi RSSI fingerprint database demonstrate that the linked neural networks trained under the proposed hierarchical stage-wise training framework can achieve a three-dimensional localization error of 8.19 m, which, to the best of the authors' knowledge, is the most accurate result ever obtained for neural network-based models trained and evaluated with the full datasets of the UJIIndoorLoc database, and that, when applied to a model based on hierarchical convolutional neural networks, the proposed training framework can also significantly reduce the three-dimensional localization error from 11.78 m to 8.71 m.