DETECTING AND UNDERSTANDING HUMAN ACTIVITY WITH MOBILE DEVICES THROUGH WIFI-BASED INDOOR LOCALIZATION TECHNOLOGY

Abstract

In this thesis, we present novel systems and techniques to track human movements and detect locations with WiFi-based information. We first present SocialProbe, a system to extract social behavior and interaction patterns of mobile users by passively monitoring WiFi probe requests and null data frames that are sent by smartphones for network control/management purposes. My second work SocialProbe proposes a Hidden Markov Models (HMM) based visitor trajectory inferring method based on passive WiFi monitoring. Moreover, we make use of the transition probability derived from existing trajectories to generate the possible movements of devices with randomized MAC addresses. In our third work, we propose EvaLoc, a WiFi fingerprint-based localization evaluation tool that helps researchers quantify the localization accuracy degradation under different conditions.