An efficient variable distance measure k-means [VDMKM] algorithm for cluster head selection in WSN

5Citations
Citations of this article
9Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Wireless sensor networks (WSNs) provide an empirical and explorative ways to handle and collection of the data in the centralized way. In this paper an efficient variable distance measure k-means [VDMKM] algorithm for cluster heads (CHs) selection in WSN has been presented and analyzed. This approach is divided into two phases. In the first phase data preprocessing has been performed. In this phase size-based weight and threshold assignment have been done on the scaling factor of 1-10. In the second phase VDMKM approach has been applied. The main benefit of this approach is the capability of smaller distance assignments automatically based on different distance measures. The distance measures used here are Euclidean, Pearson coefficient and Manhattan correlation. The complete cluster correlated delay has been calculated along with the packet delivery time. Our distance measure based automatic distance adjustment approach provides better suited distance-based packet delivery in less time. The results are more prominent in the case of Euclidean and Manhattan.

Cite

CITATION STYLE

APA

Dubey, A. K. (2019). An efficient variable distance measure k-means [VDMKM] algorithm for cluster head selection in WSN. International Journal of Innovative Technology and Exploring Engineering, 9(1), 87–92. https://doi.org/10.35940/ijitee.A3925.119119

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free