Continually Answering Constraint \pmb k-\it\bfseries NN Queries in Unstructured P2P Systems

We consider the problem of efficiently computing distributedgeographical k-\it NN queries in an unstructured peer-to-peer (P2P)system, in which each peer is managed by an individualorganization and can only communicate with its logical neighboringpeers. Such queries are based on local filter query statistics,and require as less communication cost as possible, which makes itmore difficult than the existing distributed k-\it NN queries.Especially, we hope to reduce candidate peers and degradecommunication cost. In this paper, we propose an efficient pruningtechnique to minimize the number of candidate peers to beprocessed to answer the k-\it NN queries. Our approach is especiallysuitable for continuous k-\it NN queries when updating peers,including changing ranges of peers, dynamically leaving or joiningpeers, and updating data in a peer. In addition, simulation resultsshow that the proposed approach outperforms the existing MinimumBounding Rectangle (MBR)-based query approaches, especially forcontinuous queries.