Towards self-characterization of user mobility patterns

In this paper, we investigate the individuality of mobility patterns in wireless networks composed of IEEE 802.11 access points. We propose a mobility-aware clustering algorithm that uses roaming events as the metric to evaluate the proximity to access-points (APs) without using any geographical information. The contributions of this clustering algorithm are threefold. First, it provides a sanitized image of the topological mobility of individuals. Second, it categorizes clusters as belonging to agglomerations (where individuals pauses) or to paths between agglomerations. Third, it proposes a classification of user mobility with regard to the number of places with social meanings for the individual according to the number of visited clusters. We analyze data collected within periods ranging up to 8 months and show that the differences in activeness, coverage of mobility, home locality, and size of the list of guest locations are clearly individual-related. Such results serve as a basis for the definition of future user-centric communication systems.