WiFi Based Indoor Localization: Application and Comparison of Machine Learning Algorithms

Abstract

Because of increasing the use of smartphones, it has become easier to identify location of any user. The most popular technique for outdoor positioning is the GPS signal which is commonly used in smartphones and transport vehicles. However, position detection can not be achieved indoor with GPS. Therefore, in this study, a location determination based on WiFi signal strengths was performed indoor where user could not correctly receive the GPS signal. The data includes the strengths of seven WiFi signals that provide information about four different rooms. Based on the WiFi signal strength values coming from seven different sources to smartphone, the position of the user at which room can be determined. In this study, classification was achieved for the determination of the indoor room. Six different Machine Learning (ML) methods were applied to the classification. These methods are Artificial Neural Networks (ANN), K-Nearest Neighbors (k-NN), Decision Trees (DT), Naive Bayes (NB) Classifier, Extreme Learning Machine (ELM) and Support Vector Machines (SVM). Successful results were obtained from all the methods and these results were compared with each other.