K-Means Based Cluster Formation and Head Selection Through Artificial Neural Network in MANET

Mobile ad-hoc network is a dynamic and self configuring network composed of mobile nodes that are cooperative and intelligent in nature, which forms a temporary network without any base station. Due to dynamic nature, route among source and destination is not fixed and it can change with time, this results in routing overhead, congestion and higher energy consumption in nodes. In this condition, clustering becomes very relevant in highly dense network in which the clusterhead initiates the routing instead of normal nodes. This paper proposes an efficient way for cluster formation and selection of stable cluster heads (E-CFSA) with high energy level. E-CFSA forms node cluster using k-means algorithm where distance with centroids acts as the key parameter. Afterwards, artificial neural network (ANN) is applied in each cluster to select efficient clusterhead. It also updates weight of input parameters such as mobility, packet drop, energy and number of neighbor nodes in order to minimize the errors at target neuron using back propagation algorithm. The overall process identifies and selects the nodes as clusterheads that possess longer stability and higher energy level. This procedure reduces the repetitive selection of cluster heads and re-affiliation of member nodes in a cluster. Thereafter, performance is evaluated in terms of overhead, packet delivery ratio, throughput and cluster head stability time with variation in speed of node or number of nodes in MANET. The experimental results obtained through proposed E-CFSA are compared to other existing mechanisms and a comparison is drawn. These comparative results very clearly revealed that proposed E-CFSA outperformed its peers.