Locating emergencies in a campus using wi-fi access point association data

Despite much progress in emergency management, effective techniques for real-time tracking of emergency events are still lacking. We envision a promising direction to achieve real-time emergency tracking is through widely adopted smartphones. In this paper, we explore the first step in achieving this goal, namely, locating emergency in real time using smartphones. Our main contribution is a novel approach that locates emergencies by analyzing AP (access point) association events collected from a campus Wi-Fi network. It is motivated by the observation that human behavior and mobility pattern are significantly altered in the face of emergency, which is reflected in how their smartphones associate with the APs in the network. More specifically, our approach locates emergency by discovering APs with abnormal association patterns using Extreme Value Theory (EVT). Preliminary evaluation using real data collected from a university campus network demonstrates the effectiveness of our approach.