UTMInDualSymFi: A Dual-Band Wi-Fi Dataset for Fingerprinting Positioning in Symmetric Indoor Environments
Recent studies on indoor positioning using Wi-Fi fingerprinting are motivated by the ubiquity of Wi-Fi networks and their promising positioning accuracy. Machine learning algorithms are commonly leveraged in indoor positioning works. The performance of machine learning based solutions are dependent...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Data |
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| Online Access: | https://www.mdpi.com/2306-5729/8/1/14 |
| _version_ | 1797443872979156992 |
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| author | Asim Abdullah Muhammad Haris Omar Abdul Aziz Rozeha A. Rashid Ahmad Shahidan Abdullah |
| author_facet | Asim Abdullah Muhammad Haris Omar Abdul Aziz Rozeha A. Rashid Ahmad Shahidan Abdullah |
| author_sort | Asim Abdullah |
| collection | DOAJ |
| description | Recent studies on indoor positioning using Wi-Fi fingerprinting are motivated by the ubiquity of Wi-Fi networks and their promising positioning accuracy. Machine learning algorithms are commonly leveraged in indoor positioning works. The performance of machine learning based solutions are dependent on the availability, volume, quality, and diversity of related data. Several public datasets have been published in order to foster advancements in Wi-Fi based fingerprinting indoor positioning solutions. These datasets, however, lack dual-band Wi-Fi data within symmetric indoor environments. To fill this gap, this research work presents the UTMInDualSymFi dataset, as a source of dual-band Wi-Fi data, acquired within multiple residential buildings with symmetric deployment of access points. UTMInDualSymFi comprises the recorded dual-band raw data, training and test datasets, radio maps and supporting metadata. Additionally, a statistical radio map construction algorithm is presented. Benchmark performance was evaluated by implementing a machine-learning-based positioning algorithm on the dataset. In general, higher accuracy was observed, on the 5 GHz data scenarios. This systematically collected dataset enables the development and validation of future comprehensive solutions, inclusive of novel preprocessing, radio map construction, and positioning algorithms. |
| first_indexed | 2024-03-09T13:04:22Z |
| format | Article |
| id | doaj.art-0bb8a9ae88a8478094c27660e32f266b |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-09T13:04:22Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-0bb8a9ae88a8478094c27660e32f266b2023-11-30T21:50:27ZengMDPI AGData2306-57292023-01-01811410.3390/data8010014UTMInDualSymFi: A Dual-Band Wi-Fi Dataset for Fingerprinting Positioning in Symmetric Indoor EnvironmentsAsim Abdullah0Muhammad Haris1Omar Abdul Aziz2Rozeha A. Rashid3Ahmad Shahidan Abdullah4Telecommunication Software and Systems Research Group, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai 81310, Johor Bahru, MalaysiaFaculty of Computing, Universiti Teknologi Malaysia, Skudai 81310, Johor Bahru, MalaysiaWireless Communication Centre, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai 81310, Johor Bahru, MalaysiaTelecommunication Software and Systems Research Group, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai 81310, Johor Bahru, MalaysiaTelecommunication Software and Systems Research Group, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai 81310, Johor Bahru, MalaysiaRecent studies on indoor positioning using Wi-Fi fingerprinting are motivated by the ubiquity of Wi-Fi networks and their promising positioning accuracy. Machine learning algorithms are commonly leveraged in indoor positioning works. The performance of machine learning based solutions are dependent on the availability, volume, quality, and diversity of related data. Several public datasets have been published in order to foster advancements in Wi-Fi based fingerprinting indoor positioning solutions. These datasets, however, lack dual-band Wi-Fi data within symmetric indoor environments. To fill this gap, this research work presents the UTMInDualSymFi dataset, as a source of dual-band Wi-Fi data, acquired within multiple residential buildings with symmetric deployment of access points. UTMInDualSymFi comprises the recorded dual-band raw data, training and test datasets, radio maps and supporting metadata. Additionally, a statistical radio map construction algorithm is presented. Benchmark performance was evaluated by implementing a machine-learning-based positioning algorithm on the dataset. In general, higher accuracy was observed, on the 5 GHz data scenarios. This systematically collected dataset enables the development and validation of future comprehensive solutions, inclusive of novel preprocessing, radio map construction, and positioning algorithms.https://www.mdpi.com/2306-5729/8/1/14Wi-Fi datasetindoor positioningfingerprintingdual-bandsymmetric environmentsraw data |
| spellingShingle | Asim Abdullah Muhammad Haris Omar Abdul Aziz Rozeha A. Rashid Ahmad Shahidan Abdullah UTMInDualSymFi: A Dual-Band Wi-Fi Dataset for Fingerprinting Positioning in Symmetric Indoor Environments Data Wi-Fi dataset indoor positioning fingerprinting dual-band symmetric environments raw data |
| title | UTMInDualSymFi: A Dual-Band Wi-Fi Dataset for Fingerprinting Positioning in Symmetric Indoor Environments |
| title_full | UTMInDualSymFi: A Dual-Band Wi-Fi Dataset for Fingerprinting Positioning in Symmetric Indoor Environments |
| title_fullStr | UTMInDualSymFi: A Dual-Band Wi-Fi Dataset for Fingerprinting Positioning in Symmetric Indoor Environments |
| title_full_unstemmed | UTMInDualSymFi: A Dual-Band Wi-Fi Dataset for Fingerprinting Positioning in Symmetric Indoor Environments |
| title_short | UTMInDualSymFi: A Dual-Band Wi-Fi Dataset for Fingerprinting Positioning in Symmetric Indoor Environments |
| title_sort | utmindualsymfi a dual band wi fi dataset for fingerprinting positioning in symmetric indoor environments |
| topic | Wi-Fi dataset indoor positioning fingerprinting dual-band symmetric environments raw data |
| url | https://www.mdpi.com/2306-5729/8/1/14 |
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