Indoor localization for mobile devices

This paper proposes an indoor localization system for mobile devices in urban high-rise environments. The proposed system classifies received signal strength measured from existing Wi-Fi access points, and predicts location of mobile devices based on the measured Wi-Fi signal strength and building floor plan. We collected data using different mobile devices, generated heat maps of signal strength recorded in a high-rise building for each Wi-Fi access point, and evaluated three location estimation methods. We applied clustering and Naive-Bayes algorithms to train the classifier and compared the location estimation accuracy of the three methods on the collected dataset. Experimental results show that the system can achieve an average of over 80% location prediction accuracy by clustering data into a number of location zones for the dataset.