Abstract

Abstract. There is increasing interest in location-aware systems and applications. It is important for any designer of such systems and applications to understand the nature of user and device mobility. Furthermore, an understanding of the effect of user mobility on access points (APs) is also important for designing, deploying, and managing wireless networks. Although various studies of wireless networks have provided insights into different network environments and user groups, it is often hard to apply these findings to other situations, or to derive useful abstract models. In this paper, we present a general methodology for extracting mobility information from wireless network traces, and for classifying mobile users and APs. We used the Fourier transform to convert time-dependent location information to the frequency domain, then chose the two strongest periods and used them as parameters to a classification system based on Bayesian theory. To classify mobile users, we computed diameter (the maximum distance between any two APs visited by a user during a fixed time period) and observed how this quantity changes or repeats

Citations