A mobility prediction algorithm for the seamless handoff

In recent years, many applications based on movement prediction have appeared very strongly. One of those is in the smooth handover process and resource reservation to improve Quality of Service (QoS). For effective productivity of these processes, time factor is very important. It needs exact information such as when and how long the user will go to a next location. Active Lezi algorithm and order-2 Markov model algorithm were appreciated in accuracy implementation, but the time factors were not significantly considered. In this paper, we propose a novel location prediction method constructed from a list of time and location history. A tree based on Lezi family of data compression is constructed and then, combined with Hidden Markov Model (HMM) for movement prediction driven by a sequence of time series. From our experiment, we used a part of dataset come from Dartmouth including information of 281 users. We achieved an accuracy rate of 85.52% location prediction. Moreover, the length of tree and the lost information were both reduced significantly.