Parallelized K-Means clustering algorithm for self aware mobile ad-hoc networks

Authors:
Likewin Thomas;Kiran Manjappa;B. Annappa;G. Ram Mohana Reddy
Affiliations:
National Institute of Technology Karnataka, Surathkal, Mangalore, India;National Institute of Technology Karnataka, Surathkal, Mangalore, India;National Institute of Technology Karnataka, Surathkal, Mangalore, India;National Institute of Technology Karnataka, Surathkal, Mangalore, India
Venue:
Proceedings of the 2011 International Conference on Communication, Computing & Security
Year:
2011

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Abstract

Providing Quality of Service (QoS) in Mobile Ad-hoc Network (MANET) in terms of bandwidth, delay, jitter, throughput etc., is critical and challenging issue because of node mobility and the shared medium. The work in this paper predicts the best effective cluster while taking QoS parameters into account. The proposed work uses K-Means clustering algorithm for automatically discovering clusters from large data repositories. Further, iterative K-Means clustering algorithm is parallelized using Map-Reduce technique in order to improve the computational efficiency and thereby predicting the best effective cluster. Hence, parallel K-Means algorithm is explored for finding the best effective cluster containing the hops which lies in the best cluster with the best throughput in self aware MANET.