Open Access

ARTICLE

MagneFi: Multiuser, Multi-Building and Multi-Floor Geomagnetic Field Dataset for Indoor Positioning

Imran Ashraf¹, Muhammad Usman Ali², Soojung Hur¹, Gunzung Kim¹, Yongwan Park^1,*

1 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, 38541, Korea
2 Department of Computer Science, University of Gujrat, Gujrat, 50700, Pakistan

* Corresponding Author: Yongwan Park. Email:

Computers, Materials & Continua 2022, 73(1), 1747-1768. https://doi.org/10.32604/cmc.2022.020610

Received 29 May 2021; Accepted 30 June 2021; Issue published 18 May 2022

Abstract

Indoor positioning and localization have emerged as a potential research area during the last few years owing to the wide proliferation of smartphones and the inception of location-attached services for the consumer industry. Due to the importance of precise location information, several positioning technologies are adopted such as Wi-Fi, ultrawideband, infrared, radio frequency identification, Bluetooth beacons, pedestrian dead reckoning, and magnetic field, etc. Although Wi-Fi and magnetic field-based positioning are more attractive concerning the deployment of Wi-Fi access points and ubiquity of magnetic field data, the latter is preferred as it does not require any additional infrastructure as other approaches do. Despite the advantages of magnetic field positioning, comparing the performance of positioning and localization algorithms is very difficult due to the lack of good public datasets that cover various aspects of the magnetic field data. Available datasets do not provide the data to analyze the impact of device heterogeneity, user heights, and time-specific magnetic field mutation. Moreover, multi-floor and multi-building data are available for the evaluation of state-of-the-art approaches. To overcome the above-mentioned issues, this study presents multi-user, multi-device, multi-building magnetic field data which is collected over a longer period. The dataset contains the data from five different smartphones including Samsung Galaxy S8, S9, A8, LG G6, and LG G7 for three geographically separated buildings. Three users including one female and two males collected the data for various path geometry and data collection scenarios. Moreover, the data contains the magnetic field samples collected on stairs to test multi-floor localization. Besides the magnetic field data, the data from inertial measurement unit sensors like the accelerometer, motion sensors, and barometer is provided as well.

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Keywords

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