An assessment of different optimization strategies for location tracking with an Android application on a smartphone

This paper presents a study of the efficacy of different optimization strategies for location tracking on an Android App that is run on a smartphone. The basic algorithm determines the most probable path of the user within a WiFi network by comparing raw RSSI measurements at each location with values in a fingerprint database. The investigated optimization strategies include: accounting for previous locations, increasing the number of WiFi scans per location, applying an advanced averaging technique, exploiting accelerometer data, shifting the frequency band from 2.4 to 5 GHz, and changing the position of the smartphone with respect to the body. It is shown that especially the accelerometer data allow enhancing the location estimation significantly. By combining different techniques, an average accuracy better than 2 m can be achieved