A Weighted-Range Classification Model for Localizing Cell using Crowdsource Data
Aaron Franklin Soon1, Siti Nurulain Mohd Rum2, Hamidah Ibrahim3, Rohaya Latip4, Razali Yaakob5, Lilly Suriani Affendey6
1Aaron Franklin Soon, Aaron Franklin Soon is a Ph.D Student, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia.
2Siti Nurulain Mohd Rum, Senior Lecturer, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia.
3Hamidah Ibrahim, Professor, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia.
4Rohaya Latip, Associate Professor, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia.
5Razali Yaakob, Associate Professor, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia.
6Lilly Suriani Affendey, Associate Professor, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia.
Manuscript received on 21 August 2019 | Revised Manuscript received on 11 September 2019 | Manuscript Published on 17 September 2019 | PP: 1351-1358 | Volume-8 Issue-2S8 August 2019 | Retrieval Number: B10660882S819/2019©BEIESP | DOI: 10.35940/ijrte.B1066.0882S819
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Crowdsourcing, Triangulation, Fingerprint, Weighted Range.
Scope of the Article: Classification