Predicting length of stay at WiFi hotspots

Today's smartphones provide a variety of sensors, enabling high-resolution measurements of user behavior. We envision that many services can benefit from short-term predictions of complex human behavioral patterns. While enablement of behavior awareness through sensing is a broad research theme, one possibility is in predicting how quickly a person will move through a space. Such a prediction service could have numerous applications. For one example, we imagine shop owners predicting how long a particular customer is likely to browse merchandise, and issue targeted mobile coupons accordingly - customers in a hurry can be encouraged to stay and consider discounts. Within a space of moderate size, WiFi access points are uniquely positioned to track a statistical framework for user length of stay, passively recording metrics such as WiFI signal strength (RSSI) and potentially receiving client-uploaded sensor data. In this work, we attempt to quantity this opportunity, and show that human dwell time can be predicted with reasonable accuracy, even when restricted to passively observed WiFi RSSI.