Mean Teacher based SSL Framework for Indoor Localization Using Wi-Fi RSSI Fingerprinting
Sihao Li, Zhe Tang, Kyeong Soo Kim, Jeremy S. Smith

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…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · IoT-based Smart Home Systems · Energy Efficient Wireless Sensor Networks
Methodstravel james
