# DataLoc+: A Data Augmentation Technique for Machine Learning in Room-Level Indoor Localization

arXiv: https://arxiv.org/abs/2101.10833 · HTML: https://mlsanity.com/paper/2101.10833/

## Key facts

- Cited by 5 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 21 Jan 2021.

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

- [A Survey of Indoor Localization Systems and Technologies](https://mlsanity.com/paper/1709.01015/) (2017)
- [Effectiveness of Data Augmentation in Cellular-based Localization Using Deep Learning](https://mlsanity.com/paper/1906.08171/) (2019)
- [Robust Sub-meter Level Indoor Localization - A Logistic Regression Approach](https://mlsanity.com/paper/1902.06226/) (2019)
- [Indoor Localization for IoT Using Adaptive Feature Selection: A Cascaded Machine Learning Approach](https://mlsanity.com/paper/1905.01000/) (2019)
- [Indoor Localization by Fusing a Group of Fingerprints Based on Random Forests](https://mlsanity.com/paper/1703.02185/) (2017)

## Built on by (most cited first, 5 of 5)

- [Simple and Effective Augmentation Methods for CSI Based Indoor Localization](https://mlsanity.com/paper/2211.10790/) (2022)
- [Data Augmentation Techniques for Cross-Domain WiFi CSI-based Human Activity Recognition](https://mlsanity.com/paper/2401.00964/) (2024)
- [GATE: Graph Attention Neural Networks with Real-Time Edge Construction for Robust Indoor Localization using Mobile Embedded Devices](https://mlsanity.com/paper/2507.11053/) (2025)
- [On the Multidimensional Augmentation of Fingerprint Data for Indoor Localization in A Large-Scale Building Complex Based on Multi-Output Gaussian Process](https://mlsanity.com/paper/2211.10642/) (2022)
- [Wireless Channel Aware Data Augmentation Methods for Deep Learning-Based Indoor Localization](https://mlsanity.com/paper/2408.06452/) (2024)

## Abstract (arXiv)

Indoor localization has been a hot area of research over the past two decades. Since its advent, it has been steadily utilizing the emerging technologies to improve accuracy, and machine learning has been at the heart of that. Machine learning has been increasingly used in fingerprint-based indoor localization to replace or emulate the radio map that is used to predict locations given a location signature. The prediction quality of a machine learning model primarily depends on how well the model was trained, which relies on the amount and quality of data used to train it. Data augmentation has been used to improve quality of the trained models by synthetically producing more training data, and several approaches were used in the literature that tackles the problem of lack of training data from different angles. In this paper, we propose DataLoc+, a data augmentation technique for room-level indoor localization that combines different approaches in a simple algorithm. We evaluate the technique by comparing it to the typical direct snapshot approach using data collected from a field experiment conducted in a hospital. Our evaluation shows that the model trained using the proposed technique achieves higher accuracy. We also show that the technique adapts to larger problems using a limited dataset while maintaining high accuracy.