Machine Learning Based Indoor Localization using Wi-Fi Fingerprinting
Shivam Wadhwa1, Palash Rai2, Rahul Kaushik3
1Shivam Wadhwa, Electronics and Communication, Jaypee institute of information technology, Noida, India.
2Palash Rai, Electronics and Communication, Jaypee institute of information technology, Noida, India.
3Rahul Kaushik, Electronics and Communication, Jaypee institute of information technology, Noida, India.
Manuscript received on 01 August 2019. | Revised Manuscript received on 06 August 2019. | Manuscript published on 30 September 2019. | PP: 502-506 | Volume-8 Issue-3 September 2019 | Retrieval Number: A2133058119/19©BEIESP | DOI: 10.35940/ijrte.A2133.098319
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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 aim of indoor localization is to locate the objects inside a location wirelessly. This paper reports the models that predict the location along with floor and coordinates from the WAPs (Web Access Points) signal strengths of a user who connects to the internet at a specific location which had three locations. Starting with the cleaning of data, then assigning attributes into proper data types, making subset of dataset for each location, examining each column, and normalizing WAPs rows in order to build models. Different algorithms have been used to predict the location, floor, and coordinates of a logged in user. The models that have been used in this paper are k-Nearest Neighbor (k-NN) for location prediction, random forest for floor prediction and regression with k-NN for coordinate prediction.
Index Terms: Indoor Positioning System, Wi-Fi Fingerprinting, k-NN, Random Forest, Regression, WAP.
Scope of the Article: Machine Learning