kNN queries method on uncertain data over P2P networks

There exists an inherent uncertainty on the data objects in sensor networks and P2P systems, due to imprecise measurements and network delays. In order to solve the problem, a novel k nearest neighbor query processing method on uncertain data over P2P networks which is based on k nearest neighbor query processing method on uncertain data in centralized environment was proposed. This method is based on super-peer network topology, and adopts an extended R-tree index, called P2PR-tree, to index the dataset in the distributed database for solving multi-dimensional data index in the P2P environment. Using two pruning algorithms, the number of candidate sets is reduced, and the computation costs and network overhead of kNN queries are further reduced. The experimental results are conducted to verify the high performance of our method on network costs.