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A graph clustering based method for Energy efficient clustering in wireless sensor networks

Publish Year: 1395
Type: Conference paper
Language: English
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NAECE02_047

Index date: 2 August 2017

A graph clustering based method for Energy efficient clustering in wireless sensor networks abstract

Wireless sensor networks have recently gained the attention of researchers in many challenging aspects. The most important challenge in these networks is energy conservation. Enhancing the lifetime of wireless sensors network and efficient utilizations of bandwidth are essential for the proliferation of wireless sensor network in different applications. In this paper, a graph clustering based method is proposed to optimize the lifetime of wireless sensor networks. The proposed method is a cluster based approach like LEACH. Graph clustering is used to maximize the lifetime of the network by means of rounds. The clustering algorithm is presented by considering energyconservation of the nodes through load balancing. The proposed algorithms are experimented extensively and the results are compared with the existing algorithms to demonstrate their superiority in terms of network life, energy consumption, dead sensor nodes and delivery of total data packets to the base station. The results show that the proposed method is found to be more efficient than previous works.

A graph clustering based method for Energy efficient clustering in wireless sensor networks Keywords:

A graph clustering based method for Energy efficient clustering in wireless sensor networks authors

Zusha beiranvand

Department of Computer Ashtian Branch, Islamic Azad University Buinzahra, Iran

Mehrdad Rostami

Department of Computer University of kurdistan Sanadaj, Iran

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. Yamawaki, A., M. Yamanaka, and S. Serikawa, A sensor ...
. Yu, H. and W. Xiaohui, PSO-based Energy-b alanced Double ...
. Eltaliawy, A., H. Mostafa, and Y. Ismail, Micro-scale variation- ...
. Hashim, H.A., B.O. Ayinde, and M.A. Abido, Optimal placement ...
. Nguyen Duy, T. and V. Nguyen Dinh. SSTBC: Sleep ...
. Abbasi A, Younis M. A survey on clustering algorithms ...
. Mamalis B, Gavalas D, Kon stantopoulos C, Pantziou G. ...
. B andyopadhyay S, Coyle E. An energy efficient hierarchical ...
. Low, C.P., et al., 2008. Efficient load-balanced clustering algorithms ...
. H. Tan, I. Korpeoglu, Power efficient data gathering and ...
. H. Tan, I. Korpeoglu, I. Stojmenovic, Computing localized power ...
. J. Jaromczyk, G. Toussaint, Relative neighborhood graphs and their ...
. Kuila, P., Jana, P.K., 2014. Approximati on schemes for ...
. V. Blondel, et al., Fast unfolding of communities in ...
. Xingqin Qi, et al., Laplacian centrality: A new centrality ...
. Heinzelman _ R, Chandrakasan A, Balakrishnan H. Energy- efficient ...
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