In this paper, we analyze a year long wireless network users' mobility trace data collected on ETH Zurich campus. Unlike earlier work in [4,18], 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 [8], but also demonstrate the advantages of exploiting it in performing hub-level location predictions In particular, we show that such profile based location predictions are more precise than common statistical approaches based on observed hub visitation frequencies alone.
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