An Improved Three-Layer Low-Energy Adaptive Clustering Hierarchy for Wireless Sensor Networks


Authors : K.Viji, T.Srinivasa Perumal, R.Siva Prakash ,M.Vijay Ananthkumar ,

Volume/Issue : Volume 2 - 2017, Issue 5 - May

Google Scholar : https://goo.gl/K4LTc4

Scribd : https://goo.gl/n3O4GU

Thomson Reuters ResearcherID : https://goo.gl/3bkzwv

In a wireless sensor network the topology control is used to balances the communication on sensor devices and have a response to increase the network lifetime and scalability. So conserve the energy of the sensor networks is highly concern. In this they have one approach that is Hierarchical or cluster-based design which is used to conserve the energy of the sensor networks. By this, the nodes with the higher residual energy could be used to gather data and route the information. The previous work on clustering has the twolayer hierarchy and that only few methods studied a threelayer scheme instead. But the previous work of two layer hierarchy is not efficient. Based on the three layer scheme we proposed a semi-distributed clustering approach by considering a hybrid of centralized gridding for the upperlevel head selection and distributed clustering for the lower-level head selection. The simulation results show that the proposed approach is more efficient than other distributed algorithms. Therefore, the technique presented in this paper could be further applied to large scale wireless sensor networks.

Keywords : fingerprint identification; Convolution Neural Network (CNN); fuzzy feature points; recognition rate.

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