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Distance based Cluster Head Selection Model for Optimum Number of Clusters in WSN

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Namrata Arya, Vivek Suryawanshi

Namrata Arya and Vivek Suryawanshi. Distance based Cluster Head Selection Model for Optimum Number of Clusters in WSN. International Journal of Computer Applications 156(1):58-63, December 2016. BibTeX

	author = {Namrata Arya and Vivek Suryawanshi},
	title = {Distance based Cluster Head Selection Model for Optimum Number of Clusters in WSN},
	journal = {International Journal of Computer Applications},
	issue_date = {December 2016},
	volume = {156},
	number = {1},
	month = {Dec},
	year = {2016},
	issn = {0975-8887},
	pages = {58-63},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2016912362},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Wireless sensor network is growing very much in last few decades as its useful applications. To provide more functionality it’s necessary to increase lifespan by clustering method. Clustering divides sensor nodes area into numbers of logical cluster. Each cluster has responsible node known as cluster head. This clustering and CH selection method uses lots of energy this energy can be saved by effective clustering method and improve lifespan of overall network. This work improves previous work for proper energy utilization for cluster head selection in WSN. Distance from base station is used for better clustering with optimum number of nodes within cluster. The nodes are categories into ring form with base station locate in center. A method with different probability of each node for cluster head selection is proposed. This probability of node select as cluster head is based on distance of current node from base station. The proposed method is reduces overall energy uses over other methods and performance is increased as numbers of nodes increases within large distance to base station


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Wireless Sensor network (WSN), Clustering, Energy consumption.