Processing k nearest neighbor queries in location-aware sensor networks

dc.citation.epage2881en_US
dc.citation.issue12en_US
dc.citation.spage2861en_US
dc.citation.volume87en_US
dc.citation.woscount15
dc.contributor.authorXu, Yingqien_US
dc.contributor.authorFu, Tao-Yangen_US
dc.contributor.authorLee, Wang-Chienen_US
dc.contributor.authorWinter, Julianen_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.date.accessioned2014-12-08T15:12:59Z
dc.date.available2014-12-08T15:12:59Z
dc.date.issued2007-12-01en_US
dc.description.abstractEfficient search for k nearest neighbors to a given location point (called a KNN query) is an important problem arising in a variety of sensor network applications. In this paper, we investigate in-network query processing strategies under a KNN query processing framework in location-aware wireless sensor networks. A set of algorithms, namely the geo-routing tree, the KNN boundary tree and the itinerary-based KNN algorithms, are designed in accordance with the global infrastructure-based, local infrastructure-based and infrastructure-free strategies, respectively. They have distinctive performance characteristics and are desirable under different contexts. We evaluate the performance of these algorithms under several sensor network scenarios and application requirements, and identify the conditions under which the various approaches are preferable. (C) 2007 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.sigpro.2007.05.013en_US
dc.identifier.issn0165-1684en_US
dc.identifier.journalSIGNAL PROCESSINGen_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.sigpro.2007.05.013en_US
dc.identifier.urihttps://ir.lib.nycu.edu.tw/handle/11536/10020
dc.identifier.wosnumberWOS:000249464000002
dc.language.isoen_USen_US
dc.subjectwireless sensor networksen_US
dc.subjectk nearest neighbor (KNN) queryen_US
dc.subjectnetwork infrastructureen_US
dc.titleProcessing k nearest neighbor queries in location-aware sensor networksen_US
dc.typeArticleen_US

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