Prediction of the density of active wireless device using markov model

Location management is one of the key issues in wireless networks to provide an efficient and low-cost service. Realistic modeling of user mobility is a critical research area in wireless network. Mobility data based on real human behaviors may give us the opportunity to improve wireless and Mobile services for users in many ways. At present, several mobility models are proposed based on the analysis of real traces . In this paper, we investigate the feasibility of next state prediction using sequences of previously observed state and analyze the efficiency of MARKOV MODEL . The scenario concerns servicing wireless devices by wireless access point in the Dartmouth college campus over some period of time. The performance of the method has been verified for prediction accuracy. It is found that, on average, the choice of training data leads to prediction accuracy of 78.45%, in some cases the accuracy achieves about 95.55%.