A dual mobility model with user profiling: Decoupling user mobile patterns from association patterns

Recently, the release of trace data sets from several wireless network communities has stimulated much research on empirical mobility models in wireless local area networks. Many of these models have tried to extract the user mobile features from the trace data and establish the mobility model that can be used to infer or determine user mobile patterns. However, these models have ambiguously used user association patterns and user mobile patterns in realizing mobility models, where the association patterns can be perceived directly from the trace data and the user mobile patterns can be filtered from the former. The limitation of the resulting mobility model is that it depends too much on the placement of access points (APs), and thus, it is limited to represent general user mobility. In this paper, we build a dual mobility model (DMM) to describe user association patterns and mobile patterns separately, based on the observation that user association patterns do not always reflect the true user mobile patterns in a densely deployed network but they correspond to the mobile patterns in a sparsely deployed network. Specifically, we first characterized the false-positive registration patterns that result from misunderstanding multiple AP signals. We then extracted those patterns from the original trace data to construct a dense mobility model that describes the user association patterns in dense networks. With the remaining trace data (positive mobile patterns), we built a sparse mobility model that regards the association patterns as mobile patterns. In the sparse modeling, we executed user profiling to accurately characterize user mobile patterns according to user's moving coverage and speed. Finally, we validated that the DMM model with user profiling is statistically more precise than the other mobility model.