Machine Learning Based Indoor Localization Using Wi-Fi RSSI Fingerprints: An Overview

In the era of the Internet of Things (IoT) and Industry 4.0, the indoor usage of smart devices is expected to increase, thereby making their location information more important. Based on various practical issues related to large delays, high design cost, and limited performance, conventional localiz...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.127150-127174
Main Authors: Singh, Navneet, Choe, Sangho, Punmiya, Rajiv
Format: Article
Language:English
Subjects:
Costs
Datasets
deep learning
Electronic devices
Fingerprint recognition
Fingerprinting
Fingerprints
indoor localization
Industrial applications
Internet of Things
Localization
Location awareness
Machine learning
positioning
Prediction models
received signal strength indicator
Signal strength
Smart devices
Wi-Fi
Wireless fidelity
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Summary:In the era of the Internet of Things (IoT) and Industry 4.0, the indoor usage of smart devices is expected to increase, thereby making their location information more important. Based on various practical issues related to large delays, high design cost, and limited performance, conventional localization techniques are not practical for indoor IoT applications. In recent years, many researchers have proposed a wide range of machine learning (ML)-based indoor localization approaches using Wi-Fi received signal strength indicator (RSSI) fingerprints. This survey attempts to provide a summarized investigation of ML-based Wi-Fi RSSI fingerprinting schemes, including data preprocessing, data augmentation, ML prediction models for indoor localization, and postprocessing in ML, and compare their performance. Any ML-based study is heavily reliant on datasets. Therefore, we dedicate a significant portion of this survey to the discussion of dataset collection and open-source datasets. To provide good direction for future research, we discuss the current challenges and potential solutions related to ML-based indoor localization systems.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3111083