Intra-clustering communication enhancement in WSN by using skillful methodologies

Abstract

Member nodes in a wireless sensor networks are split into many virtual sets called cluster. Each cluster will be formed of a cluster head and cluster members. Clustering techniques are an energy efficient process that prolong the network lifetime. Intra-cluster communication is the energy efficient major factor in clustering protocols. The dissipated energy in intra-cluster will primary depends on the communication distance between member nodes and its cluster head. Therefore, reducing of energy that have been consumed in data transmission between ordinary nodes and its belonging cluster header, is consider as one of the important topics that have been taken into consideration by researchers. Clustering technique in WSN is one of the robust techniques that are utilizing to enhance consumed energy. Traditional clustering technique is not convenient with the animated nature of the WSN. A Fuzzy C-Mean Clustering (FCM) algorithm is proposed, this method has given momentum to improve network status and promote the state of sustainability of energy consumed by sensors nodes. Decision tree algorithm (DTA) which falls under the category of supervised machine learning algorithms is utilized to select Cluster Head (CH). The CH electing is done in such a way that reduces the intra-cluster communication distance; also, this method has limit the amount of consumed energy in the entire network.An event driven data model that depends on FCM with DTA which is named as Voronoi Fuzzy Clustering with Decision Tree Algorithm (VFDTA) is adopted in this study. The evaluation metrics the performance of VFDTA protocol is better than well-known protocols (LEACH, SEP and Z-SEP) in terms of network stability, consumed energy and network lifetime.