A Survey of Deep Learning-Assisted Indoor Localization with Wi-Fi Fingerprinting: Current Status and Research Challenges 


Vol. 46,  No. 5, pp. 848-862, May  2021
10.7840/kics.2021.46.5.848


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  Abstract

In the recent 5G era, according to the rapid development of Internet-of-Things technologies along with smart devices and expansion of its application, the indoor localization techniques using wireless signals have been extensively studied. It is a very challenging task to develop an indoor localization technique with high location accuracy and low complexity in various indoor environments. Among many indoor localization techniques, fingerprint technique using Wi-Fi signals has been received the greatest interest from academia and industry because location estimation is possible without the need for installing separate equipment and constructing infrastructure. Recently, the accuracy of indoor localization has been largely improved as machine learning and deep learning technologies, which have been rapidly developed, have been incorporated into Wi-Fi fingerprints. Therefore, in this paper, we introduce the state-of-the-art trends and pros-and-cons of indoor localization techniques based on Wi-Fi fingerprint utilizing machine learning and deep learning technologies. Furthermore, we discuss new research directions and issues based on the described trends of indoor localization techniques for Wi-Fi fingerprint.

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  Cite this article

[IEEE Style]

H. Noh, Y. Oh, N. Lee, W. Shin, "A Survey of Deep Learning-Assisted Indoor Localization with Wi-Fi Fingerprinting: Current Status and Research Challenges," The Journal of Korean Institute of Communications and Information Sciences, vol. 46, no. 5, pp. 848-862, 2021. DOI: 10.7840/kics.2021.46.5.848.

[ACM Style]

Hea-Min Noh, Yongjeong Oh, Namyoon Lee, and Wonjae Shin. 2021. A Survey of Deep Learning-Assisted Indoor Localization with Wi-Fi Fingerprinting: Current Status and Research Challenges. The Journal of Korean Institute of Communications and Information Sciences, 46, 5, (2021), 848-862. DOI: 10.7840/kics.2021.46.5.848.

[KICS Style]

Hea-Min Noh, Yongjeong Oh, Namyoon Lee, Wonjae Shin, "A Survey of Deep Learning-Assisted Indoor Localization with Wi-Fi Fingerprinting: Current Status and Research Challenges," The Journal of Korean Institute of Communications and Information Sciences, vol. 46, no. 5, pp. 848-862, 5. 2021. (https://doi.org/10.7840/kics.2021.46.5.848)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880