Location management in PCN by movement prediction of the mobile host

The mobile host's mobility profile, in a personal communication network (PCN) environment, is modeled. It is argued that, for a majority of mobile hosts (MHs) for most of the time, the movement profile repeats on a day-to-day basis. The next movement strongly depends on the present location and the time of the day. In this paper, such a pattern for every individual MHs is learned. The model is not static and re-learning is initiated as the behavior of the mobile host changes. Thus the model assumes that the past patterns will repeat in future and a past causal relationship (i.e., next state depends on previous state) continue into the future. A copy of the model is uploaded at the home location register (HLR). This facilitates the system to predict to a high degree of accuracy the location of a MH. During the course of learning, as the model gets perfected, the frequency of updates decreases as well as the probability of success in paging improves. The model is continuously verified locally and re-learning is initiated when a shift in mobility pattern is detected. The validity of the proposed model was verified through simulations.