Classification algorithm based on nodes similarity for MANETs

Abstract

This article describes an algorithm of classification by similarity of nodes in a MANET (Clustering). To optimize a network performance without influencing others, we must act only on the cluster structure. Any additional calculation clutters more the system. To overcome this limitation, a strong classification method is needed. The purpose of classification algorithms is the search for an optimal partition. This optimum is obtained iteratively refining an initial pattern randomly selected by reallocating objects around mobile centers. In order to partition the nodes into clusters, we used this technique (iterative reallocation) from the well known k-means algorithm. The algorithm conception is based on the k-means method that we improved and adapted to make it suitable for mobile ad hoc networks. The algorithm is implemented on OLSR giving birth to a new routing protocol: OLSRKmeans.