High-performance spatial indexing for location-based services

Much attention has been accorded to Location-Based Services and location tracking, a necessary component in active, trigger-based LBS applications. Tracking the location of a large population of moving objects requires very high update and query performance of the underlying spatial index. In this paper we investigate the performance and scalability of three main-memory based spatial indexing methods under dynamic update and query loads: an R-tree, a ZB-tree, and an array/hashtable method. By leveraging the LOCUS performance evaluation testbed and the City Simulator dynamic spatial data generator, we are able to demonstrate the scalability of these methods and determine the maximum population size supported by each method, a useful parameter for capacity planning by wireless carriers.