A weighted-range classification model for localizing cell using crowdsource data
The vast amount of mobile smartphone users provides an infinite source of data for crowdsourcing. Crowdsourcing provides an economical method of gathering data to cover a large geographical area compared to traditional methods. However, the inaccurate predictions for base station localization derive...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Blue Eyes Intelligence Engineering and Sciences Publication
2019
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| Online Access: | http://psasir.upm.edu.my/id/eprint/80523/ http://psasir.upm.edu.my/id/eprint/80523/1/DATA.pdf |
| _version_ | 1848858914018295808 |
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| author | Mohd Rum, Siti Nurulain Soon, Aaron Franklin Affendey, Lilly Suriani Yaakob, Razali Latip, Rohaya Ibrahim, Hamidah |
| author_facet | Mohd Rum, Siti Nurulain Soon, Aaron Franklin Affendey, Lilly Suriani Yaakob, Razali Latip, Rohaya Ibrahim, Hamidah |
| author_sort | Mohd Rum, Siti Nurulain |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The vast amount of mobile smartphone users provides an infinite source of data for crowdsourcing. Crowdsourcing provides an economical method of gathering data to cover a large geographical area compared to traditional methods. However, the inaccurate predictions for base station localization derived from mobile crowdsourcing impacts its effectiveness for use in radio planning. Therefore, the purpose of this study is to design a model that can yield a more accurate localization through the introduction of a rule-based weighted classification. The methodology deployed is a permutation series based on fingerprint of the cell site with weightage derived from rule-based classification. DeLaunay triangulation and Voronoi diagrams are used to determine the positions of the existing base stations and the prediction of new site location respectively. The expected results are better accuracy of the classification model in the localization prediction of the base station leading to a more accurate prediction of new site location. |
| first_indexed | 2025-11-15T12:21:00Z |
| format | Article |
| id | upm-80523 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T12:21:00Z |
| publishDate | 2019 |
| publisher | Blue Eyes Intelligence Engineering and Sciences Publication |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-805232020-11-10T07:20:02Z http://psasir.upm.edu.my/id/eprint/80523/ A weighted-range classification model for localizing cell using crowdsource data Mohd Rum, Siti Nurulain Soon, Aaron Franklin Affendey, Lilly Suriani Yaakob, Razali Latip, Rohaya Ibrahim, Hamidah The vast amount of mobile smartphone users provides an infinite source of data for crowdsourcing. Crowdsourcing provides an economical method of gathering data to cover a large geographical area compared to traditional methods. However, the inaccurate predictions for base station localization derived from mobile crowdsourcing impacts its effectiveness for use in radio planning. Therefore, the purpose of this study is to design a model that can yield a more accurate localization through the introduction of a rule-based weighted classification. The methodology deployed is a permutation series based on fingerprint of the cell site with weightage derived from rule-based classification. DeLaunay triangulation and Voronoi diagrams are used to determine the positions of the existing base stations and the prediction of new site location respectively. The expected results are better accuracy of the classification model in the localization prediction of the base station leading to a more accurate prediction of new site location. Blue Eyes Intelligence Engineering and Sciences Publication 2019 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/80523/1/DATA.pdf Mohd Rum, Siti Nurulain and Soon, Aaron Franklin and Affendey, Lilly Suriani and Yaakob, Razali and Latip, Rohaya and Ibrahim, Hamidah (2019) A weighted-range classification model for localizing cell using crowdsource data. International Journal of Recent Technology and Engineering, 8 (2S8). pp. 1351-1358. ISSN 2277-3878 https://www.ijrte.org/wp-content/uploads/papers/v8i2S8/B10660882S819.pdf 10.35940/ijrte.B1066.0882S819 |
| spellingShingle | Mohd Rum, Siti Nurulain Soon, Aaron Franklin Affendey, Lilly Suriani Yaakob, Razali Latip, Rohaya Ibrahim, Hamidah A weighted-range classification model for localizing cell using crowdsource data |
| title | A weighted-range classification model for localizing cell using crowdsource data |
| title_full | A weighted-range classification model for localizing cell using crowdsource data |
| title_fullStr | A weighted-range classification model for localizing cell using crowdsource data |
| title_full_unstemmed | A weighted-range classification model for localizing cell using crowdsource data |
| title_short | A weighted-range classification model for localizing cell using crowdsource data |
| title_sort | weighted-range classification model for localizing cell using crowdsource data |
| url | http://psasir.upm.edu.my/id/eprint/80523/ http://psasir.upm.edu.my/id/eprint/80523/ http://psasir.upm.edu.my/id/eprint/80523/ http://psasir.upm.edu.my/id/eprint/80523/1/DATA.pdf |