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Location management strategies can be categorized into two main groups: offline (static) and online (dynamic) schemes. In offline schemes, the network has a unique behavior for all users, such as the current GSM networks. On the other hand, in online schemes, different network topologies are considered for different users. These topologies are closely related to the movement pattern and calling behavior of each user. Because online schemes are much more complex than offline ones, they require more network computation capabilities. Thus, offline schemes are more widely implemented. However, these schemes must use minimal network resources to manage user tracking, and should not require massive computation. Traditional location management techniques for mobile networks are very conservative and there is a need for more 'intelligent' techniques' to enable future networks to better predict the location of users in the network based on their past movement history. Such strategies usually have two different stages: modeling the users' behaviour patterns and invoking an intelligent algorithm to use the extracted model in order to locate the users. Several algorithms and strategies have been suggested to solve the stated problem in different ways. The use of Markov models is one of the most popular techniques used for solving this problem. Other techniques include can be classified as: history based, distance based, movement based, and time based. This talk will address the dynamic mobility management problem and will suggest few computationally effective solutions that provide reasonable accurate results.