Three Effective Top-Down Clustering Algorithms for Location Database Systems

Recent technological advances in mobile communication systems have made explosive growth in the number of mobile device users worldwide. One of the most important issues in designing a mobile computing system is location management of users. The hierarchical systems had been proposed to solve the scalability problem in location management. The scalability problem occurs when there are too many users for a mobile system to handle, as the system is likely to react slow or even get down due to late updates of the location databases. In this paper, we propose a top-down clustering algorithm for hierarchical location database systems in a wireless network. A hierarchical location database system employs a tree structure. The proposed algorithm uses a top-down approach and utilizes the number of visits to each cell made by the users along with the movement information between a pair of adjacent cells. We then present a modified algorithm by incorporating the exhaustive method when there remain a few levels of the tree to be processed. We also propose a capacity constraint top-down clustering algorithm for more realistic environments where a database has a capacity limit. By the capacity of a database we mean the maximum number of mobile device users in the cells that can be handled by the database. This algorithm reduces a number of databases used for the system and improves the update performance. The experimental results show that the proposed, top-down, modified top-down, and capacity constraint top-down clustering algorithms reduce the update cost by 17.0%, 18.0%, 24.1%, the update time by about 43.0%, 39.0%, 42.3%, respectively. The capacity constraint algorithm reduces the average number of databases used for the system by 23.9% over other algorithms.