Indoor positioning technologies without offline fingerprinting map: A survey

Beakcheol Jang, Hyunjung Kim

Research output: Contribution to journalArticlepeer-review

43 Citations (Scopus)

Abstract

Fingerprint-based wireless indoor positioning approaches are widely used for location-based services because wireless signals, such as Wi-Fi and Bluetooth, are currently pervasive in indoor spaces. The working principle of fingerprinting technology is to collect the fingerprints from an indoor environment, such as a room or a building, in advance, create a fingerprint map, and use this map to estimate the user’s current location. The fingerprinting technology is associated with a high level of accuracy and reliability. However, the fingerprint map must be entirely re-created, not only when the Wi-Fi access points are added, modified, or removed, but also when the interior features, such as walls or even furniture, are changed, owing to the nature of the wireless signals. Many researchers have realized the problems in the fingerprinting technology and are conducting studies to address them. In this paper, we review the indoor positioning technologies that do not require the construction of offline fingerprint maps. We categorize them into simultaneous localization and mapping; inter/extrapolation; and crowdsourcing-based technologies, and describe their algorithms and characteristics, including advantages and disadvantages. We compare them in terms of our own parameters: accuracy, calculation time, versatility, robustness, security, and participation. Finally, we present the future research direction of the indoor positioning techniques. We believe that this paper provides valuable information on recent indoor localization technologies without offline fingerprinting map construction.

Original languageEnglish
Article number8451859
Pages (from-to)508-525
Number of pages18
JournalIEEE Communications Surveys and Tutorials
Volume21
Issue number1
DOIs
Publication statusPublished - 2019 Jan 1

Bibliographical note

Funding Information:
Manuscript received January 23, 2018; revised July 18, 2018; accepted August 23, 2018. Date of publication August 30, 2018; date of current version February 22, 2019. This work was supported by the National Research Foundation of Korea grant funded by the Korea Government under Grant NRF-2016R1D1A1B03930815. (Corresponding author: Beakcheol Jang.) The authors are with the Department of Computer Science, Sangmyung University, Seoul 03016, South Korea (e-mail: bjang@smu.ac.kr). Digital Object Identifier 10.1109/COMST.2018.2867935

Publisher Copyright:
© 2018 IEEE.

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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