UGC Approved Journal no 63975

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 6 Issue 6
June-2019
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR1908D52


Registration ID:
309856

Page Number

1206-1212

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Title

GMM-DT: A Novel Model of Gaussian Mixture Model-based Clustering using Dijkstra Algorithm in WSN

Abstract

— Wireless sensors network as an energy consumption technique has been widely discussed in several studies. However, factors such as cluster formation or Cluster Head (CH) assignment node approach have a dramatic effect on network performance as reducing energy efficiency involves restricted resources or network data traffic. For this purpose, we have developed a new model which consists of the Gaussian mixture model and the Dijkstra tree. The Gaussian Mixture model is used for clustering to generate clusters. The key purpose of this approach is to reduce sensor node communication by using clustering techniques. In this paper, Gaussian Mixture is used to measure a log-likelihood for sensor nodes to obtain an optimal result of a cluster. The related cluster heads may then be selected. The cluster head selection is done based on distance from a base station and the energy level of nodes. Dijkstra shortest path tree algorithm is used later for establishing the communication among the CHs hence making a secure connection for nodes. Simulation findings show the efficiency in terms of energy consumption or cellular network life of our proposed algorithm over the existing popular protocol. The results are compared with the R-LEACH protocol in MATLAB and parameters like nodes dead rounds, energy consumption has been calculated. The balancing of network demands among clusters has increase energy efficiency or effectively prolong network life.

Key Words

WSN, IoT, Clustering, CH Selection, Residual Energy (RE), Gaussian Mixture Model, Dijkstra Tree

Cite This Article

"GMM-DT: A Novel Model of Gaussian Mixture Model-based Clustering using Dijkstra Algorithm in WSN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.1206-1212, June-2019, Available :http://www.jetir.org/papers/JETIR1908D52.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"GMM-DT: A Novel Model of Gaussian Mixture Model-based Clustering using Dijkstra Algorithm in WSN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp1206-1212, June-2019, Available at : http://www.jetir.org/papers/JETIR1908D52.pdf

Publication Details

Published Paper ID: JETIR1908D52
Registration ID: 309856
Published In: Volume 6 | Issue 6 | Year June-2019
DOI (Digital Object Identifier):
Page No: 1206-1212
Country: -, --, India .
Area: Engineering
ISSN Number: 2349-5162


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