Energy efficient exact kNN search in wireless broadcast environments

The advances in wireless communication and decreasing costs of mobile devices have enabled users to access desired information at any time. Coupled with positioning technologies like GPS, this opens up an exciting domain of location based services, allowing a mobile user to query for objects based on its current position. Main bottlenecks in such infrastructures are the draining of power of the mobile devices and the limited network bandwidth available. To alleviate these problems, <i>broadcasting</i> spatial information about relevant objects has been widely accepted as an efficient mechanism. An important class of queries for such an infrastructure is the <i>k</i>-nearest neighbor (<i>k</i>NN) queries, in which users are interested in <i>k</i> closest objects to their position. In this paper, we describe mechanisms to perform <i>exact</i> <i>k</i>NN search on conventional sequential-access R-trees, and optimize established <i>k</i>NN search algorithms. We also propose a novel use of histograms for guiding the search and derive analytical results on maximum queue size and node access count. In addition, we discuss the effects of different broadcast organizations on search performance and challenge the traditional use of Depth-First (<i>dfs</i>) organization. We also extend our mechanisms to support <i>k</i>NN search with non-spatial constraints. While we demonstrate our ideas using a broadcast index, they are equally applicable to any kind of sequential access medium like tertiary tape storage. We validate our mechanisms through an extensive experimental analysis and present our findings.