QR*-Tree: An Adaptive Space-Partitioning Index for Monitoring Moving Objects

A continuous range query over moving objects continually retrieves the moving objects that are currently within a given query region of interest. Most existing approaches assume that moving objects continually communicate with the server to report their current locations and the server updates the results of queries continuously. However, this assumption degrades the system performance because the communication cost and the server workload increase when the number of moving objects and queries becomes huge. The Query Region tree (QR-tree) is a query indexing structure, which helps the server cooperate with the moving objects efficiently by utilizing the available computational resources of the moving objects to improve the overall system performance. In this paper, we propose a variant of the QR-tree, namely, QR-tree, which helps reduce (i) the amount of location-update stream generated from moving object and (ii) the server work load for query evaluation. Through a series of comprehensive simulations, we verify the efficiency of the QR-tree in terms of the wireless communication cost and the server workload.