An Empirical Activity Model for WLAN Users

Understanding user behavior in wireless environments is useful for a variety of reasons ranging from the design of better sleep algorithms for components of mobile devices to appropriately provisioning the wireless network itself to better serve the user. Our work goes in a different direction from prior work on WLAN modeling and attempts to undersand the protocol independent behavior of users by developing packet-level models for user activity using diverse training data. Additionally we validate the derived model using a stochastic similarity metric adapted from human control strategy modeling and present a novel way to compare traces using this metric.