A Dynamic Adaptive Caching Location Strategy by Location Database Clustering

In mobile environment, an important method to improve the performance of locating mobile users is caching users location information. In addition, location databases in hierarchical structure can be clustered to reduce the total cost of location management. However, previous caching strategies aim at a single user, which causes caching to be inefficient, and the existing location database clustering approaches do not consider caching users location information. A dynamic adaptive caching location （DACaL） strategy based on location database clustering is proposed for mass mobile users. DACaL strategy utilizes the advantages of both caching and clustering techniques to reduce the total cost of location management in mobile environment, which is divided into the following two steps. In LDB-clustering, location databases are clustered by mining mobile users moving pattern to determine the caching level and reduce the location management cost. LDB clustering is a set covering problem. In dynamic-adaptive-caching, location databases are reorganized based on clustering result, location information is cached, and bypass pointers are created between adjacent clusters to shorten signal traveling path and to abate times of querying location databases. Dynamic adaptive caching is a TSP problem. Experiments show that DACaL strategy can reduce the total cost of location management and has better performance than other strategies.