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A weighted-range classification model for localizing cell using crowdsource data


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


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.

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Additional Metadata

Item Type: Article
Divisions: Faculty of Computer Science and Information Technology
DOI Number: https://doi.org/10.35940/ijrte.B1066.0882S819
Publisher: Blue Eyes Intelligence Engineering and Sciences Publication
Keywords: Crowdsourcing; Triangulation; Fingerprint; Weighted range
Depositing User: Ms. Nuraida Ibrahim
Date Deposited: 10 Nov 2020 07:20
Last Modified: 10 Nov 2020 07:20
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.35940/ijrte.B1066.0882S819
URI: http://psasir.upm.edu.my/id/eprint/80523
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