WiFi Fingerprinting-based Indoor Positioning with Machine Learning Algorithms
With the rapid advances of mobile devices, location-based services have received significant attention. Among the available services, finding the exact position of a person, especially indoors, is a challenging problem. For indoor environments, using WiFi-based technology for positioning purposes is...
Αποθηκεύτηκε σε:
| Κύριοι συγγραφείς: | , , , |
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| Μορφή: | Conference paper |
| Γλώσσα: | English |
| Έκδοση: |
Khoa học và Kỹ thuật
2023
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| Διαθέσιμο Online: | https://scholar.dlu.edu.vn/handle/123456789/2007 |
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| Thư viện lưu trữ: | Thư viện Trường Đại học Đà Lạt |
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| Περίληψη: | With the rapid advances of mobile devices, location-based services have received significant attention. Among the available services, finding the exact position of a person, especially indoors, is a challenging problem. For indoor environments, using WiFi-based technology for positioning purposes is reasonable due to its utilization of existing WiFi infrastructure. In this paper, we implement and compare the positioning results of three machine learning algorithms such as support vector machine, decision tree, and random forest. The algorithms are applied to a multi-condition WiFi fingerprinting dataset which was conducted in an office room where different environmental conditions are considered. The results show that the random forest achieves the best classification result with an accuracy of over 85%, while the two others get an approximate accuracy of 80%. |
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