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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...

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Main Authors: Mohd Rum, Siti Nurulain, Soon, Aaron Franklin, Affendey, Lilly Suriani, Yaakob, Razali, Latip, Rohaya, Ibrahim, Hamidah
Format: Article
Language:English
Published: Blue Eyes Intelligence Engineering and Sciences Publication 2019
Online Access:http://psasir.upm.edu.my/id/eprint/80523/1/DATA.pdf
http://psasir.upm.edu.my/id/eprint/80523/
https://www.ijrte.org/wp-content/uploads/papers/v8i2S8/B10660882S819.pdf
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spelling my.upm.eprints.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
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
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.
format Article
author Mohd Rum, Siti Nurulain
Soon, Aaron Franklin
Affendey, Lilly Suriani
Yaakob, Razali
Latip, Rohaya
Ibrahim, Hamidah
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
author_facet Mohd Rum, Siti Nurulain
Soon, Aaron Franklin
Affendey, Lilly Suriani
Yaakob, Razali
Latip, Rohaya
Ibrahim, Hamidah
author_sort Mohd Rum, Siti Nurulain
title 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_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_sort weighted-range classification model for localizing cell using crowdsource data
publisher Blue Eyes Intelligence Engineering and Sciences Publication
publishDate 2019
url http://psasir.upm.edu.my/id/eprint/80523/1/DATA.pdf
http://psasir.upm.edu.my/id/eprint/80523/
https://www.ijrte.org/wp-content/uploads/papers/v8i2S8/B10660882S819.pdf
_version_ 1683232229360140288
score 13.211869

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