Application of parallel K-means clustering algorithm for prediction of optimal path in self aware mobile ad-hoc networks with link stability

Abstract

Providing Quality of Service (QoS) in terms of bandwidth, delay, jitter, throughput etc., for Mobile Ad-hoc Network (MANET) which is the autonomous collection of nodes, is challenging issue because of node mobility and the shared medium. This work is to predict the Optimal link based on the link stability which is the number of contacts between 2 pair of nodes that can be effectively applied for prediction of optimal effective path while taking QoS parameters into account to reach the destination using the application of K-Means clustering algorithm for automatically discovering clusters from large data repositories which is parallelized using Map-Reduce technique in order to improve the computational efficiency and thereby predicting the optimal effective path from source to sink. The work optimizes the previous result by pre-assigning task for finding the best stable link in MANET and then work is explored only on that stable link hence, by doing so we are able to predict the optimal path in more time efficient way. � 2011 Springer-Verlag.