Simulation of a mobility prediction scheme based on neuro-fuzzy theory in mobile computing

Mobility prediction is an important issue for efficient management in mobile computing. Whenever users move to a new location, mobile systems should know about their location change so that the connection is not interrupted. In this paper, we focus on the simulation of a user mobility prediction scheme based on neuro-fuzzy theory. Our scheme predicts future locations from the movement patterns of users accumulated over several days or months, i.e., the user's location history. In order to model users' movements, we use a neuro-fuzzy inference system. Whenever a user moves to a new location, the system autonomously learns various movement patterns. The prediction of the future location to which users will move is obtained by the approximate reasoning of the neuro-fuzzy inference system. This approach can derive an appropriate future location from learned location information, even if the mobile systems do not know the actual location history. Simulation results show that our prediction scheme has high accuracy for various users' patterns. In addition, our scheme is applicable, with considerable prediction accuracy, to users whose history has not been learned by the neuro-fuzzy inference system.