A mobile ad hoc network is a collection of wireless nodes that can dynamically be set up anywhere and anytime without using any pre-existing network infrastructure. Several algorithms like Lowest ID, Least Cluster-head Change, Highest in-degree, Weighted Clustering Algorithm, IWCA, neural network based etc. have been proposed for clustering of nodes. They do not examine the combined effect of parameters like battery power, neighbours of node and mobility on cluster formation. Although these factors can be considered as inputs to a neural network, training the network and choosing the training algorithm is a computationally intensive hence time consuming step. In this research we address this issue by calculating computing a computationally un-intensive factor for deciding cluster-heads. This factor works in any environment and takes into account environmental changes, hence proving useful when nodes are added or subtracted dynamically from the ad-hoc network. This factor calculation could easily be built into software and can be deployed for cluster-head calculation in any ad-hoc environment with no underlying assumptions. Since we need fast calculations when the clusters change in the ad-hoc environment, coming up with a deciding factor which we can calculate fast and efficiently prevent connection breaks, dropped packets, and routing anomalies.

MANET Cluster heads