Improving clustering techniques in wireless sensor networks using thinning process
Abstract
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
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