An Improved PSOGSA for Clustering and Routing in WSNs

Wireless sensor network (WSN) is an integration of sensing, communicating, computing in a board range environment. Efficient energy consumption becomes the most challenging task for sensor nodes. The clustering and routing techniques are promising methods to resolve the issue and extend the network’s lifespan. The clustering technique is defined as grouping data into classes, every cluster sharing a high degree of similarity in between them, and each cluster being dissimilar with others. This technique is the best data processing model for WSN, and it controls the redundant data inside the network. The nomination of the appropriate cluster head is a major factor in the clustering technique. The object of this proposed paper is to equipoise the energy of the clustering nodes and route the data from cluster head to sink. We propose an improved particle swarm optimization gravitational search algorithm for clustering and routing in WSNs. Here clustering algorithm makes equal energy in the entire network by the uniform distribution of cluster head, routing algorithm decides the ideal routing path to convey data packet between cluster head and sink. The proposed paper integrates the exploration capacity of GSA and the exploitation capability of PSO. Detailed simulation performs using MATLAB based simulator in terms of residual energy, network lifespan, and convergence rate. In comparing our proposed algorithm to other existing algorithms, it outperforms significantly.