Energy Efficient Weighted Clustering Algorithm in Wireless Sensor Networks

Mallanagouda Patil

Energy Efficient Weighted Clustering Algorithm in Wireless Sensor Networks

Mallanagouda Patil *

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Energy Efficient Weighted Clustering Algorithm in Wireless Sensor Networks

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Abstract

With the advancement in communication and internet technologies, recently there have been many research efforts in the area of Wireless Sensor Networks (WSNs) to conserve energy. Clustering mechanisms have been applied to WSNs to enhance the network performance while reducing the necessary energy consumption. The goal of Weighted Clustering Algorithm (WCA) is to determine the cluster heads dynamically based on a combined weight metric that includes one or more parameters such as node degree, distances with respect to a nodes neighbors, node speed and the time spent as a cluster head. In this work, we have proposed a refined and improved version of WCA known as Energy Efficient Weighted Clustering Algorithm (EEWCA) to prolong the network lifetime by reducing energy consumption. EEWCA is designed and simulated with additional constraint on energy for the selection of cluster heads. Both the WCA and EEWCA schemes have been simulated using MATLAB. The proposed EEWCA behaves better than WCA for longer system lifetime.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Mallanagouda Patil. 2017. "Energy Efficient Weighted Clustering Algorithm in Wireless Sensor Networks". Global Journal of Computer Science and Technology - E: Network, Web & Security GJCST-E Volume 17 (GJCST Volume 17 Issue E2).

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GJCST Volume 17 Issue E2

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

GJCST-E Classification I.4.8

C.2.1

Submission ReceivedDecember 9, 2016
Peer Review Double Blind
Handling Editor
Accepted January 5, 2017
Published January 15, 2017

Version of record

v1.2

Issue date

May 19, 2017

Language

English

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Article Matrices

Total Score: 142

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: Mallanagouda Patil, Rajashekhar C. Biradar (PhD/Dr. count: 0)

View Count (all-time): 486

Total Views (Real + Logic): 2670

Total Downloads (simulated): 243

Publish Date: 2017 01, Sun

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Total Views: 2.7K

Total Downloads: 243

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