Mobility Prediction Using Mobile User Profiles

In this paper we propose a new approach for mobility prediction. It is based on the notion of mobile-user profile which corresponds to frequent similar movements of a user. Such a profile is defined, in the neighbourhood graph of a cellular network, as a set of similar sequences of crossed cells from one source cell to one destination cell. We propose an algorithmic approach to identify such profiles from real cellular network datasets and we show how to use them for an efficient mobility prediction. Simulation results show that the global approach is significantly efficient in predicting both random and regular movements.