Mean Teacher based SSL Framework for Indoor Localization Using Wi-Fi RSSI Fingerprinting

TL;DR

This paper presents a semi-supervised deep learning framework using the Mean Teacher model to improve Wi-Fi fingerprint-based indoor localization, effectively utilizing unlabeled data for better accuracy in multi-building and multi-floor environments.

Contribution

It introduces a novel semi-supervised learning approach with wireless access point selection and noise injection, enhancing localization performance and database expansion capabilities.

Findings

Significant improvement in floor-level coordinate estimation accuracy.

Up to 10.99% and 8.98% enhancement in localization performance.

Effective utilization of unlabeled fingerprints for continual database expansion.

Abstract

Wi-Fi fingerprinting is widely applied for indoor localization due to the widespread availability of Wi-Fi devices. However, traditional methods are not ideal for multi-building and multi-floor environments due to the scalability issues. Therefore, more and more researchers have employed deep learning techniques to enable scalable indoor localization. This paper introduces a novel semi-supervised learning framework for neural networks based on wireless access point selection, noise injection, and Mean Teacher model, which leverages unlabeled fingerprints to enhance localization performance. The proposed framework can manage hybrid in/outsourcing and voluntarily contributed databases and continually expand the fingerprint database with newly submitted unlabeled fingerprints during service. The viability of the proposed framework was examined using two established deep-learning models with the UJIIndoorLoc database. The experimental results suggest that the proposed framework significantly improves localization performance compared to the supervised learning-based approach in terms of floor-level coordinate estimation using EvAAL metric. It shows enhancements up to 10.99% and 8.98% in the former scenario and 4.25% and 9.35% in the latter, respectively with additional studies highlight the importance of the essential components of the proposed framework.