  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.
