# Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges

arXiv: https://arxiv.org/abs/1610.05424 · HTML: https://mlsanity.com/paper/1610.05424/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2016.

## Built on by (most cited first, 8 of 8)

- [LaMAR: Benchmarking Localization and Mapping for Augmented Reality](https://mlsanity.com/paper/2210.10770/) (2022)
- [Deep Learning Methods for Fingerprint-Based Indoor Positioning: A Review](https://mlsanity.com/paper/2205.14935/) (2022)
- [AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges](https://mlsanity.com/paper/2501.14970/) (2025)
- [Estimating indoor occupancy through low-cost BLE devices](https://mlsanity.com/paper/2102.03351/) (2021)
- [Using Synthetic Data to Enhance the Accuracy of Fingerprint-Based Localization: A Deep Learning Approach](https://mlsanity.com/paper/2105.01903/) (2021)
- [WiFi Based Distance Estimation Using Supervised Machine Learning](https://mlsanity.com/paper/2208.07190/) (2022)
- [Neural 5G Indoor Localization with IMU Supervision](https://mlsanity.com/paper/2402.09948/) (2024)
- [From Global to Local: Cluster-Aware Learning for Wi-Fi Fingerprinting Indoor Localisation](https://mlsanity.com/paper/2601.05650/) (2026)

## Abstract (arXiv)

Wireless Local Area Network (WLAN) has become a promising choice for indoor positioning as the only existing and established infrastructure, to localize the mobile and stationary users indoors. However, since WLAN has been initially designed for wireless networking and not positioning, the localization task based on WLAN signals has several challenges. Amongst the WLAN positioning methods, WLAN fingerprinting localization has recently achieved great attention due to its promising results. WLAN fingerprinting faces several challenges and hence, in this paper, our goal is to overview these challenges and the state-of-the-art solutions. This paper consists of three main parts: 1) Conventional localization schemes; 2) State-of-the-art approaches; 3) Practical deployment challenges. Since all the proposed methods in WLAN literature have been conducted and tested in different settings, the reported results are not equally comparable. So, we compare some of the main localization schemes in a single real environment and assess their localization accuracy, positioning error statistics, and complexity. Our results depict illustrative evaluation of WLAN localization systems and guide to future improvement opportunities.