Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges
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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.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Oct 2016.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 0; 2024: 1; 2025: 1; 2026: 1.
8 papers.
- 2022LaMAR: Benchmarking Localization and Mapping for Augmented Realitycited by 98
- 2022Deep Learning Methods for Fingerprint-Based Indoor Positioning: A Reviewcited by 8
- 2025AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challengescited by 5
- 2021Estimating indoor occupancy through low-cost BLE devicescited by 3
- 2021Using Synthetic Data to Enhance the Accuracy of Fingerprint-Based Localization: A Deep Learning Approachcited by 3
- 2022WiFi Based Distance Estimation Using Supervised Machine Learningcited by 1
- 2024Neural 5G Indoor Localization with IMU Supervisioncited by 1
- 2026From Global to Local: Cluster-Aware Learning for Wi-Fi Fingerprinting Indoor Localisationcited by 0
- 2026From Global to Local: Cluster-Aware Learning for Wi-Fi Fingerprinting Indoor Localisation9 Jan 2026
- 2025AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges24 Jan 2025
- 2024Neural 5G Indoor Localization with IMU Supervision15 Feb 2024
- 2022LaMAR: Benchmarking Localization and Mapping for Augmented Reality19 Oct 2022
- 2022WiFi Based Distance Estimation Using Supervised Machine Learning15 Aug 2022
- 2022Deep Learning Methods for Fingerprint-Based Indoor Positioning: A Review30 May 2022
- 2021Using Synthetic Data to Enhance the Accuracy of Fingerprint-Based Localization: A Deep Learning Approach5 May 2021
- 2021Estimating indoor occupancy through low-cost BLE devices30 Jan 2021