Optimizing parallel itineraries for KNN query processing in wireless sensor networks

Tao Young Fu, Wen Chih Peng, Wang-chien Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Spatial queries for extracting data from wireless sensor networks are important for many applications, such as environmental monitoring and military surveillance. One such query is K Nearest Neighbor (KNN) query that facilitates sampling of monitored sensor data in correspondence with a given query location. Recently, itinerary-based KNN query processing techniques, that propagate queries and collect data along a pre-determined itinerary, have been developed concurrently [12][14]. These research works demonstrate that itinerary-based KNN query processing algorithms are able to achieve better energy efficiency than other existing algorithms. However, how to derive itineraries based on different performance requirements remains a challenging problem. In this paper, we propose a new itinerary-based KNN query processing technique, called PCIKNN, that derives different itineraries aiming at optimizing two performance criteria, response latency and energy consumption.The performance of PCIKNN is analyzed mathematically and evaluated through extensive experiments. Experimental results show that PCIKNN has better performance and scalability than the state-of-the-art.

Original languageEnglish (US)
Title of host publicationCIKM 2007 - Proceedings of the 16th ACM Conference on Information and Knowledge Management
Pages391-400
Number of pages10
DOIs
StatePublished - Dec 1 2007
Event16th ACM Conference on Information and Knowledge Management, CIKM 2007 - Lisboa, Portugal
Duration: Nov 6 2007Nov 9 2007

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other16th ACM Conference on Information and Knowledge Management, CIKM 2007
CountryPortugal
CityLisboa
Period11/6/0711/9/07

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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