Daily life oriented indoor localization by fusion of smartphone sensors and Wi-Fi

Abstract

Smartphones are leading among the fastest-growing technologies. With their nu merous features, smartphones are the best assistants to users in their lives on several counts. However, a smartphone still requires an extensive configuration to assist every user efficiently and effectively. In this thesis, we are motivated to develop a system that makes a smartphone self-configure automatically depending on its place. This has been well established for outdoor environments with contributions of GPS (Global Positioning System). However, GPS does not provide accurate data in indoor envi ronments. Hence, in this thesis, we aim to determine the exact place of a smartphone in a room by exploiting on-device sensors and Wi-Fi services. The key point of our study is that it entirely works on the smartphone. In accordance with our motivation, sensors data and Wi-Fi RSSI values were collected from fixed places via Data Collec tion Application which we developed on an Android smartphone. A fusion fingerprint database was created. Five supervised machine learning algorithms were evaluated on the fingerprint database in terms of classification accuracy and process time. The best performance was obtained from Decision Tree Classifier with 98% accuracy rate on 20% of training samples. Predictive power of used features were studied to specify which sensors are more meaningful for distinguishing indoor places from each other. Depending on model evaluation results, a Data Classification Application was devel oped on the same Android smartphone to generate a dedicated decision tree for each different room. Tests were carried out in three different rooms to show that more than 80% accuracy was achieved in finding the correct place in each room.