A Novel Valued Tolerance Rough Set and Decision Rules Method for Indoor Positioning Using WiFi Fingerprinting
In recent years, due to the ubiquitous presence of WiFi access points in buildings, the WiFi fingerprinting method has become one of the most promising approaches for indoor positioning applications. However, the performance of this method is vulnerable to changes in indoor environments. To tackle t...
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Multidisciplinary Digital Publishing Institute
2023
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| Truy cập trực tuyến: | https://scholar.dlu.edu.vn/handle/123456789/2440 |
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oai:scholar.dlu.edu.vn:123456789-24402023-06-09T06:47:32Z A Novel Valued Tolerance Rough Set and Decision Rules Method for Indoor Positioning Using WiFi Fingerprinting Duong-Bao, N. He, J. Thi, L. N. Nguyễn, Hữu Khánh Lee, S. W. Indoor Positioning System In recent years, due to the ubiquitous presence of WiFi access points in buildings, the WiFi fingerprinting method has become one of the most promising approaches for indoor positioning applications. However, the performance of this method is vulnerable to changes in indoor environments. To tackle this challenge, in this paper, we propose a novel WiFi fingerprinting method that uses the valued tolerance rough set theory–based classification method. In the offline phase, the conventional received signal strength (RSS) fingerprinting database is converted into a decision table. Then a new fingerprinting database with decision rules is constructed based on the decision table, which includes the credibility degrees and the support object set values for all decision rules. In the online phase, various classification levels are applied to find out the best match between the RSS values in the decision rules database and the measured RSS values at the unknown position. The experimental results compared the performance of the proposed method with those of the nearest-neighbor-based and the random statistical methods in two different test cases. The results show that the proposed method greatly outperforms the others in both cases, where it achieves high accuracy with 98.05% of right position classification, which is approximately 50.49% more accurate than the others. The mean positioning errors at wrong estimated positions for the two test cases are 1.71 m and 1.99 m, using the proposed method. 2023-06-05T08:32:10Z 2023-06-05T08:32:10Z 2022 Journal article Bài báo đăng trên tạp chí thuộc ISI, bao gồm book chapter https://scholar.dlu.edu.vn/handle/123456789/2440 en Sensors Multidisciplinary Digital Publishing Institute |
| institution |
Thư viện Trường Đại học Đà Lạt |
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Thư viện số |
| language |
English |
| topic |
Indoor Positioning System |
| spellingShingle |
Indoor Positioning System Duong-Bao, N. He, J. Thi, L. N. Nguyễn, Hữu Khánh Lee, S. W. A Novel Valued Tolerance Rough Set and Decision Rules Method for Indoor Positioning Using WiFi Fingerprinting |
| description |
In recent years, due to the ubiquitous presence of WiFi access points in buildings, the WiFi fingerprinting method has become one of the most promising approaches for indoor positioning applications. However, the performance of this method is vulnerable to changes in indoor environments. To tackle this challenge, in this paper, we propose a novel WiFi fingerprinting method that uses the valued tolerance rough set theory–based classification method. In the offline phase, the conventional received signal strength (RSS) fingerprinting database is converted into a decision table. Then a new fingerprinting database with decision rules is constructed based on the decision table, which includes the credibility degrees and the support object set values for all decision rules. In the online phase, various classification levels are applied to find out the best match between the RSS values in the decision rules database and the measured RSS values at the unknown position. The experimental results compared the performance of the proposed method with those of the nearest-neighbor-based and the random statistical methods in two different test cases. The results show that the proposed method greatly outperforms the others in both cases, where it achieves high accuracy with 98.05% of right position classification, which is approximately 50.49% more accurate than the others. The mean positioning errors at wrong estimated positions for the two test cases are 1.71 m and 1.99 m, using the proposed method. |
| format |
Journal article |
| author |
Duong-Bao, N. He, J. Thi, L. N. Nguyễn, Hữu Khánh Lee, S. W. |
| author_facet |
Duong-Bao, N. He, J. Thi, L. N. Nguyễn, Hữu Khánh Lee, S. W. |
| author_sort |
Duong-Bao, N. |
| title |
A Novel Valued Tolerance Rough Set and Decision Rules Method for Indoor Positioning Using WiFi Fingerprinting |
| title_short |
A Novel Valued Tolerance Rough Set and Decision Rules Method for Indoor Positioning Using WiFi Fingerprinting |
| title_full |
A Novel Valued Tolerance Rough Set and Decision Rules Method for Indoor Positioning Using WiFi Fingerprinting |
| title_fullStr |
A Novel Valued Tolerance Rough Set and Decision Rules Method for Indoor Positioning Using WiFi Fingerprinting |
| title_full_unstemmed |
A Novel Valued Tolerance Rough Set and Decision Rules Method for Indoor Positioning Using WiFi Fingerprinting |
| title_sort |
novel valued tolerance rough set and decision rules method for indoor positioning using wifi fingerprinting |
| publisher |
Multidisciplinary Digital Publishing Institute |
| publishDate |
2023 |
| url |
https://scholar.dlu.edu.vn/handle/123456789/2440 |
| _version_ |
1768306434535063552 |