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Sensors 2015, 15(3), 5474-5503; doi:10.3390/s150305474

Distributed Efficient Similarity Search Mechanism in Wireless Sensor Networks

School of Electrical and Computer Engineering, RMIT University, Melbourne, VIC 3000, Australia
These authors contributed equally to this work.
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Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 15 December 2014 / Revised: 14 February 2015 / Accepted: 25 February 2015 / Published: 5 March 2015
(This article belongs to the Section Sensor Networks)
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Abstract

The Wireless Sensor Network similarity search problem has received considerable research attention due to sensor hardware imprecision and environmental parameter variations. Most of the state-of-the-art distributed data centric storage (DCS) schemes lack optimization for similarity queries of events. In this paper, a DCS scheme with metric based similarity searching (DCSMSS) is proposed. DCSMSS takes motivation from vector distance index, called iDistance, in order to transform the issue of similarity searching into the problem of an interval search in one dimension. In addition, a sector based distance routing algorithm is used to efficiently route messages. Extensive simulation results reveal that DCSMSS is highly efficient and significantly outperforms previous approaches in processing similarity search queries. View Full-Text
Keywords: Wireless Sensor Networks; distributed data centric storage; similarity search; range query; K-nearest neighbor query; sector based distance routing Wireless Sensor Networks; distributed data centric storage; similarity search; range query; K-nearest neighbor query; sector based distance routing
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Ahmed, K.; Gregory, M.A. Distributed Efficient Similarity Search Mechanism in Wireless Sensor Networks. Sensors 2015, 15, 5474-5503.

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