ESA: an efficient and stable approach to querying reverse k-nearest-neighbor of moving objects

In this work, we study how to improve the efficiency and stability of querying reverse k-nearest-neighbor (RkNN) for moving objects. An approach named as ESA is presented in this paper. Different from the existing approaches, ESA selects k objects as pruning reference objects for each time of pruning. In this way, its greatly improves the query efficiency. ESA also reduces the communication cost and enhances the stability of the server by adaptively adjusting the objects' safe regions. Experimental results verify the performance of our proposed approach.