K-Means and Fuzzy based Hybrid Clustering Algorithm for WSN
Abstract
Wireless Sensor Networks (WSN) acquired a lot
of attention due to their widespread use in monitoring hostile
environments, critical surveillance and security applications. In
these applications, usage of wireless terminals also has grown
significantly. Grouping of Sensor Nodes (SN) is called clustering
and these sensor nodes are burdened by the exchange of messages
caused due to successive and recurring re-clustering, which
results in power loss. Since most of the SNs are fitted with nonrechargeable
batteries, currently researchers have been concentrating
their efforts on enhancing the longevity of these nodes. For
battery constrained WSN concerns, the clustering mechanism has
emerged as a desirable subject since it is predominantly good at
conserving the resources especially energy for network activities.
This proposed work addresses the problem of load balancing
and Cluster Head (CH) selection in cluster with minimum energy
expenditure. So here, we propose hybrid method in which cluster
formation is done using unsupervised machine learning based kmeans
algorithm and Fuzzy-logic approach for CH selection.
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