Home > Archive > 2012 > Volume 2 Number 4 (Aug. 2012) >
IJMLC 2012 Vol.2(4): 506-510 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.177

A Novel Fuzzy Diffusion Approach for Improving Energy Efficiency in Wireless Sensor Networks

Faraneh Zarafshan, Abbas Karimi, and S. A. R. Al-Haddad

Abstract—Directed Diffusion is a Data Centric routing protocol. In data centric protocols data is named based on the attribute-value pairs. Directed diffusion protocol includes flooding the interested data, establishing direction paths by using gradients set-up and selecting one or more direction paths to reinforce the data stream. The main difficulty of Directed Diffusion is flooding in which there is some communication overhead with some nodes which are naturally unable to coordinate in monitoring the interested event. We use a fuzzy logic controller to reduce the communication overheads during flooding and routing the data stream from the source(s) to a sink node. The fuzzy logic controller evaluates the potentiality of intermediate nodes to coordinate in mission, based on each node’s traffic load, energy residual and size of data which can be maintained. The simulation results show that new Fuzzy diffusion approach produces at most 56.5% and in average 28.81% successful results and has improved the total energy residual at least 19.38% and in average 1888.87% in comparison with directed diffusion protocol.

Index Terms—Wireless sensor networks, fuzzy logic, directed diffusion, energy efficiency.

F. Zarafshan and A. Karimi are with the Department of Computer Engineering, Faculty of Engineering, Arak Brach, Islamic Azad University, Arak, Iran (e-mail: fzarafshan@gmail.com; akarimi@iau-arak.com).
S. A. R. Al-Haddad is with Department of Computer and Communication Systems Engineering, Faculty of Engineering, Putra University, Serdang, Malaysia (e-mail: SAR@eng.upm.edu.my).

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Cite:Faraneh Zarafshan, Abbas Karimi, and S. A. R. Al-Haddad, "A Novel Fuzzy Diffusion Approach for Improving Energy Efficiency in Wireless Sensor Networks," International Journal of Machine Learning and Computing vol.2, no. 4, pp. 506-510, 2012.

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


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