一种基于位置数据库聚类的动态适应缓存位置信息策略

A Dynamic Adaptive Caching Location Strategy by Location Database Clustering

移动环境中提高定位移动用户性能的一个重要方法是缓存用户的位置信息.然而已经提出的缓存策略针对的是单个用户,造成缓存的效率不高.针对群体用户提出了一种基于位置数据库聚类的动态适应缓存位置信息(DACaL)策略.其中位置数据库聚类算法通过挖掘群体移动用户的运动模式对位置数据库进行聚类,以确定缓存层次和降低位置管理的代价.动态适应缓存位置信息算法根据聚类结果对位置数据库进行重组,在相邻聚类之间缓存位置信息,建立旁路指针,以缩短消息传输的路径和减少查询位置数据库的次数.实验表明,DACaL策略能够有效地降低总体代价,性能上优于相关策略.

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.