Comparative Analysis of ABC & GWO Algorithm for Wireless Sensor Network

The popularity of Wireless Sensor Networks (WSN) has increased rapidly and tremendously due to the vast potential of the sensor networks to connect the physical world with the virtual world. Since sensor devices rely on battery power and node energy and may be placed in hostile environments, so replacing them becomes a difficult task. Thus, improving the energy of these networks i.e. network lifetime becomes important. The thesis provides methods for clustering and cluster head selection to WSN to improve energy efficiency using a fuzzy logic controller. It presents a comparison between the different methods on the basis of the network lifetime. It compares existing ABC optimization method with the Gray wolf optimization (GWO) algorithm for different size of networks and different scenario. It provides cluster head selection method with good performance and reduced computational complexity. In addition, it also proposes GWO as an algorithm for clustering of WSN which would result in improved performance with faster convergence.