Cluster Density of Dependent Thinning Distributed Clustering Class of Algorithms in Ad Hoc Deployed Wireless Networks

Cluster Density of Dependent Thinning Distributed Clustering Class of Algorithms in Ad Hoc Deployed Wireless Networks

Distributed clustering is widely used in ad hoc deployed wireless networks. Distributed clustering algorithms like DMAC, HEED, MEDIC, ANTCLUST-based, and EDCR produce well-distributed Cluster Heads (CHs) using dependent thinning techniques where a node’s decision to be a CH depends on the decision o...

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Main Authors: Sankalpa Gamwarige, Chulantha Kulasekere
Format: Article
Language:English
Published: Hindawi Limited 2012-01-01
Series:Journal of Computer Networks and Communications
Online Access:http://dx.doi.org/10.1155/2012/781275
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spelling doaj-7eb767a3c4dc4f16a81408dac9a8b2d62020-11-24T22:59:53ZengHindawi LimitedJournal of Computer Networks and Communications2090-71412090-715X2012-01-01201210.1155/2012/781275781275Cluster Density of Dependent Thinning Distributed Clustering Class of Algorithms in Ad Hoc Deployed Wireless NetworksSankalpa Gamwarige0Chulantha Kulasekere1Department of Electronic and Telecommunication Engineering, University of Moratuwa, Moratuwa 10400, Sri LankaDepartment of Electronic and Telecommunication Engineering, University of Moratuwa, Moratuwa 10400, Sri LankaDistributed clustering is widely used in ad hoc deployed wireless networks. Distributed clustering algorithms like DMAC, HEED, MEDIC, ANTCLUST-based, and EDCR produce well-distributed Cluster Heads (CHs) using dependent thinning techniques where a node’s decision to be a CH depends on the decision of its neighbors. An analytical technique to determine the cluster density of this class of algorithms is proposed. This information is required to set the algorithm parameters before a wireless network is deployed. Simulation results are presented in order to verify the analytical findings.http://dx.doi.org/10.1155/2012/781275
collection DOAJ
language English
format Article
sources DOAJ
author Sankalpa Gamwarige
Chulantha Kulasekere
spellingShingle Sankalpa Gamwarige
Chulantha Kulasekere
Cluster Density of Dependent Thinning Distributed Clustering Class of Algorithms in Ad Hoc Deployed Wireless Networks
Journal of Computer Networks and Communications
author_facet Sankalpa Gamwarige
Chulantha Kulasekere
author_sort Sankalpa Gamwarige
title Cluster Density of Dependent Thinning Distributed Clustering Class of Algorithms in Ad Hoc Deployed Wireless Networks
title_short Cluster Density of Dependent Thinning Distributed Clustering Class of Algorithms in Ad Hoc Deployed Wireless Networks
title_full Cluster Density of Dependent Thinning Distributed Clustering Class of Algorithms in Ad Hoc Deployed Wireless Networks
title_fullStr Cluster Density of Dependent Thinning Distributed Clustering Class of Algorithms in Ad Hoc Deployed Wireless Networks
title_full_unstemmed Cluster Density of Dependent Thinning Distributed Clustering Class of Algorithms in Ad Hoc Deployed Wireless Networks
title_sort cluster density of dependent thinning distributed clustering class of algorithms in ad hoc deployed wireless networks
publisher Hindawi Limited
series Journal of Computer Networks and Communications
issn 2090-7141
2090-715X
publishDate 2012-01-01
description Distributed clustering is widely used in ad hoc deployed wireless networks. Distributed clustering algorithms like DMAC, HEED, MEDIC, ANTCLUST-based, and EDCR produce well-distributed Cluster Heads (CHs) using dependent thinning techniques where a node’s decision to be a CH depends on the decision of its neighbors. An analytical technique to determine the cluster density of this class of algorithms is proposed. This information is required to set the algorithm parameters before a wireless network is deployed. Simulation results are presented in order to verify the analytical findings.
url http://dx.doi.org/10.1155/2012/781275
work_keys_str_mv AT sankalpagamwarige clusterdensityofdependentthinningdistributedclusteringclassofalgorithmsinadhocdeployedwirelessnetworks
AT chulanthakulasekere clusterdensityofdependentthinningdistributedclusteringclassofalgorithmsinadhocdeployedwirelessnetworks
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