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Sensors 2017, 17(6), 1317; doi:10.3390/s17061317

Improving Multidimensional Wireless Sensor Network Lifetime Using Pearson Correlation and Fractal Clustering

1
PPGIA, University of Fortaleza (UNIFOR), Fortaleza 60811-905, Brazil
2
Computer Engineering, Federal University of Ceará (UFC), Campus de Sobral, Sobral 62010-560, Brazil
3
Department of Computer Science, Federal University of Ceará (UFC), Fortaleza 60440-900, Brazil
4
National Institute of Telecommunications (Inatel), Santa Rita do Sapucaí 37540-000, Brazil
5
Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã 6201-001, Portugal
6
ITMO University, St. Petersburg 197101, Russia
7
University of Fortaleza (UNIFOR), Fortaleza 60811-905, Brazil
Current address: Federal University of Ceará (UFC), Rua Estanislau Frota, s/n - Bloco de Tecnologia, Campus do Mucambinho—Centro, Sobral–CE 62010-560, Brazil.
*
Author to whom correspondence should be addressed.
Academic Editors: Sungrae Cho, Takeo Fujii, Joon-Sang Park and Mohamed F. Younis
Received: 13 February 2017 / Revised: 18 April 2017 / Accepted: 28 April 2017 / Published: 7 June 2017
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Abstract

An efficient strategy for reducing message transmission in a wireless sensor network (WSN) is to group sensors by means of an abstraction denoted cluster. The key idea behind the cluster formation process is to identify a set of sensors whose sensed values present some data correlation. Nowadays, sensors are able to simultaneously sense multiple different physical phenomena, yielding in this way multidimensional data. This paper presents three methods for clustering sensors in WSNs whose sensors collect multidimensional data. The proposed approaches implement the concept of multidimensional behavioral clustering. To show the benefits introduced by the proposed methods, a prototype has been implemented and experiments have been carried out on real data. The results prove that the proposed methods decrease the amount of data flowing in the network and present low root-mean-square error (RMSE). View Full-Text
Keywords: wireless sensor network; fractal clustering; multidimensional similarity measure; energy efficiency; multidimensional clustering; Pearson correlation wireless sensor network; fractal clustering; multidimensional similarity measure; energy efficiency; multidimensional clustering; Pearson correlation
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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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MDPI and ACS Style

Almeida, F.R.; Brayner, A.; Rodrigues, J.J.P.C.; Maia, J.E.B. Improving Multidimensional Wireless Sensor Network Lifetime Using Pearson Correlation and Fractal Clustering. Sensors 2017, 17, 1317.

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