Life time enhancement of wireless Sensor Network using fuzzy c-means clustering algorithm

Pramod Kumar; Ashvini Chaturvedi

doi:10.1109/ECS.2014.6892786

Life time enhancement of wireless Sensor Network using fuzzy c-means clustering algorithm

Pramod Kumar, Ashvini Chaturvedi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

The major issues in wireless Sensor Networks (WSNs) are efficient uses of limited resources and appropriate routing of network paths under severely constrained energy scenarios. To overcome these issues; k-means and fuzzy c-means algorithms are investigated to form clusters and for subsequent selection of cluster heads. For all these clusters; selection of cluster head is done based on member sensor nodes residual energy status (RES) and estimation of Euclidean distances. Depending upon the Euclidean distance measure between the sink node and center of gravity of clusters; these clusters are classified into five types. Further, RES estimations are presented for cluster heads as well simple sensor network nodes.

Original language	English
Title of host publication	2014 International Conference on Electronics and Communication Systems, ICECS 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479923205
DOIs	https://doi.org/10.1109/ECS.2014.6892786
Publication status	Published - 2014
Event	2014 International Conference on Electronics and Communication Systems, ICECS 2014 - Coimbatore, India Duration: 13-02-2014 → 14-02-2014

Conference

Conference	2014 International Conference on Electronics and Communication Systems, ICECS 2014
Country/Territory	India
City	Coimbatore
Period	13-02-14 → 14-02-14

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering
Hardware and Architecture
Computer Networks and Communications

Access to Document

10.1109/ECS.2014.6892786

Cite this

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title = "Life time enhancement of wireless Sensor Network using fuzzy c-means clustering algorithm",

abstract = "The major issues in wireless Sensor Networks (WSNs) are efficient uses of limited resources and appropriate routing of network paths under severely constrained energy scenarios. To overcome these issues; k-means and fuzzy c-means algorithms are investigated to form clusters and for subsequent selection of cluster heads. For all these clusters; selection of cluster head is done based on member sensor nodes residual energy status (RES) and estimation of Euclidean distances. Depending upon the Euclidean distance measure between the sink node and center of gravity of clusters; these clusters are classified into five types. Further, RES estimations are presented for cluster heads as well simple sensor network nodes.",

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language = "English",

booktitle = "2014 International Conference on Electronics and Communication Systems, ICECS 2014",

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Kumar, P & Chaturvedi, A 2014, Life time enhancement of wireless Sensor Network using fuzzy c-means clustering algorithm. in 2014 International Conference on Electronics and Communication Systems, ICECS 2014., 6892786, Institute of Electrical and Electronics Engineers Inc., 2014 International Conference on Electronics and Communication Systems, ICECS 2014, Coimbatore, India, 13-02-14. https://doi.org/10.1109/ECS.2014.6892786

Life time enhancement of wireless Sensor Network using fuzzy c-means clustering algorithm. / Kumar, Pramod; Chaturvedi, Ashvini.
2014 International Conference on Electronics and Communication Systems, ICECS 2014. Institute of Electrical and Electronics Engineers Inc., 2014. 6892786.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Chaturvedi, Ashvini

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N2 - The major issues in wireless Sensor Networks (WSNs) are efficient uses of limited resources and appropriate routing of network paths under severely constrained energy scenarios. To overcome these issues; k-means and fuzzy c-means algorithms are investigated to form clusters and for subsequent selection of cluster heads. For all these clusters; selection of cluster head is done based on member sensor nodes residual energy status (RES) and estimation of Euclidean distances. Depending upon the Euclidean distance measure between the sink node and center of gravity of clusters; these clusters are classified into five types. Further, RES estimations are presented for cluster heads as well simple sensor network nodes.

AB - The major issues in wireless Sensor Networks (WSNs) are efficient uses of limited resources and appropriate routing of network paths under severely constrained energy scenarios. To overcome these issues; k-means and fuzzy c-means algorithms are investigated to form clusters and for subsequent selection of cluster heads. For all these clusters; selection of cluster head is done based on member sensor nodes residual energy status (RES) and estimation of Euclidean distances. Depending upon the Euclidean distance measure between the sink node and center of gravity of clusters; these clusters are classified into five types. Further, RES estimations are presented for cluster heads as well simple sensor network nodes.

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PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 13 February 2014 through 14 February 2014

ER -

Life time enhancement of wireless Sensor Network using fuzzy c-means clustering algorithm

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