%0 Conference Proceedings
%T Improving clustering techniques in wireless sensor networks using thinning process
%+ Méthodes et modèles pour les réseaux (METHODES-SAMOVAR)
%+ Département Réseaux et Services de Télécommunications (TSP - RST)
%+ Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux (SAMOVAR)
%+ Centre National de la Recherche Scientifique (CNRS)
%+ Department of Computer Science and Engineering [Kanpur] (CSE)
%A Becker, Monique
%A Gupta, Ashish
%A Marot, Michel
%A Singh, Harmeet
%Z Part 5: Communication and Computer Networks
%< avec comité de lecture
%Z 11646
%( Lecture Notes in Computer Science
%B Performance Evaluation of Computer and Communication Systems (PERFORM)
%C Vienne, Austria
%Y Karin Anna Hummel
%Y Helmut Hlavacs
%Y Wilfried Gansterer
%I Springer
%3 Performance Evaluation of Computer and Communication Systems. Milestones and Future Challenges
%V LNCS-6821
%P 203-214
%8 2010-10-14
%D 2010
%R 10.1007/978-3-642-25575-5_17
%K Sensor networks
%K Clustering
%K Thinning process
%Z Computer Science [cs]/Networking and Internet Architecture [cs.NI]Conference papers
%X We propose a rapid cluster formation algorithm using a thinning technique : rC-MHP(rapid Clustering inspired from Matérn Hard-Core Process). In order to prove its performance, it is compared with a well known cluster formation heuristic: Max-Min. Experimental results show that rC-MHP outperforms Max-Min in terms of messages needed to choose the cluster head, cluster head maintenance and memory requirement, comprehensively in sparse as well as in dense networks. We show that rC-MHP has a scalable behavior and it is very easy to implement. rC-MHP can be used as an efficient clustering technique
%G English
%Z TC 6
%Z TC 7
%Z WG 6.3
%Z WG 7.3
%2 https://hal.science/hal-01347786/document
%2 https://hal.science/hal-01347786/file/978-3-642-25575-5_17_Chapter.pdf
%L hal-01347786
%U https://hal.science/hal-01347786
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