Hybrid Algorithm For Energy Efficient Cluster Head Identification in Wireless Sensor Networks

Abstract

The Wireless Sensor Networks (WSN) is a self organizing network with random deployment of wireless nodes that connects each other for effective monitoring and data transmission. The cluster of nodes, shares valid information by the pre programmed sensors, requires an effective cluster head for the achievement of effective energy consumption. The clustering a technique employed to group the collection of nodes for data transmission and each node is assigned with a cluster head. The WSN nodes possess limited energy and employs effective energy management techniques to address the aforementioned concern. The identification of cluster head utilizing a better topological management technique and the routing of data among the nodes using better efficient routing algorithms yields a better energy efficient wireless sensor networks. The major concern with the identification of the cluster head is the consideration of energy consumption and hence this paper proposes an hybrid model which forms an energy efficient cluster head in the Wireless Sensor Network. The proposed model is an hybridization of Glowworm Swarm Optimization (GSO) and Artificial Bee Colony (ABC) algorithm for the better identification of cluster head. The performance of the proposed model is compared with the existing techniques and an energy analysis is performed and is proved to be more efficient than the existing model withnormalized energy of 5.35% better value and reduction of time complexity upto 1.46%. Above all, the proposed model is 16% ahead of alive node count when compared with the existing methodologies.