Open AccessMachine Learning Based Indoor Localization using Wi-Fi and Smartphone
Author(s) -
Zulqarnain Khokhar,
Murtaza Ahmed Siddiqi
Publication year - 2020
Publication title -
jisr on computing/journal of independent studies and research computing
Language(s) - English
Resource type - Journals
eISSN - 2412-0448
pISSN - 1998-4154
DOI - 10.31645/06
Subject(s) - random forest , decision tree , computer science , gradient boosting , classifier (uml) , artificial intelligence , machine learning , boosting (machine learning) , decision tree learning , mobile device , real time computing , operating system
Wi-Fi based indoor positioning with the help of access points and smart devices have become an integral part in finding a device or a person’s location. Wi-Fi based indoor localization technology has been among the most attractive field for researchers for a number of years. In this paper, we have presented Wi-Fi based in-door localization using three different machine-learning techniques. The three machine learning algorithms implemented and compared are Decision Tree, Random Forest and Gradient Boosting classifier. After making a fingerprint of the floor based on Wi-Fi signals, mentioned algorithms were used to identify device location at thirty different positions on the floor. Random Forest and Gradient Boosting classifier were able to identify the location of the device with accuracy higher than 90%. While Decision Tree was able to identify the location with accuracy a bit higher than 80%.