Discovering People’s Life Pattern From WiFi Scanlists

The prevalence of smart phones equipped with various sensors enables pervasive capturing users’mobility data (GPS, GSM network, WiFi, etc.), which represents approximate whereabouts of users. The trajectory data which contains individual location information provides new opportunities for understanding the intent and regularity of users. In recent years, more and more researches have been made for mining mobility regularity and living habits. However, through data mining and other technical methods, individual sensitive information is mined out in the meantime, which raised concerns about privacy leakage.In this study, we are attempting to discover people’s life patterns within the protection of privacy by further studying the users’ mobile trajectory formed by WiFi scanlists. The main contribution of this paper is,1) Propose a method of using graph model to represent user’s mobility. We extract visit places from WiFi scanlists and then build a mobile graph based on the trajectory.2) The mobility of users can be discovered by the frequent trajectory patterns and their characteristics in the active areas.3) The cluster method based on the features of location is designed to recognize users’ home and work place. And we understand users’daily routine through the analysis of their behaviors in both places.