On Profiling Mobility and Predicting Locations of Campus-Wide Wireless Network Users
In this paper, we analyze a year long wireless network users’ mobility trace data collected on ETH Zurich campus. Unlike earlier work in [9], [21], [35], we profile the movement pattern of wireless users and predict their locations. More specifically, we show that each network user regularly visits a list of places, such as a building (also referred to as “hubs”) with some probability. The daily list of hubs, along with their corresponding visit probabilities, are referred to as a mobility profile. We also show that over a period of time (e.g., a week), a user may repeatedly follow a mixture of mobility profiles with certain probabilities associated with each of the profiles. Our analysis of the mobility trace data not only validate the existence of our so-called sociological orbits [13], but also demonstrate the advantages of exploiting it in performing hub-level location predictions. Moreover, such profile based location predictions are found not only to be more precise than a common statistical approach based on observed hub visitation frequencies, but also shown to incur a much lower overhead. We further illustrate the benefit of profiling users’ mobility by discussing relevant work and suggesting applications in different types of wireless networks, including mobile ad hoc networks.