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The Proportional Investigation on SVM Using Distributed Clustering Algorithm for WSN

D. Manikandan, V . Kathiresan


The emergence of Wireless Sensor Networks (WSN) as one of the dominant technology trends in the recent decades has posed numerous unique challenges to researchers. The key networking challenges in sensor networks are: (a) supporting multi-hop communication while limiting radio operation to conserve power, (b) solve the traffic problems (c) geographic routing challenges and (d) monitoring and maintenance. In this work we focus on the wireless sensor network become known as locations under heavy traffic load. Nodes in such areas quickly deplete energy resources, leading to disruption in network services. This problem is common for data collection scenarios in which Cluster Heads (CH) have a heavy burden of gathering and relaying information. The relay load on CHs especially intensifies as the distance to the sink decreases. To balance the traffic load and the energy consumption in the network, the CH role should be rotated among all nodes and the cluster sizes should be carefully determined at different parts of the network. In this work as a new tool, SVM has been widely used in the Wireless Sensor Networks (WSN) compared with the distributed clustering algorithm that determines suitable cluster sizes depending on the hop distance to the data sink, while achieving approximate equalization of node lifetimes and reduced energy consumption levels.


Data Mining, Cluster Head, SVM, Clustered WSN.

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