Wolf Nature and Neural Network Based WSN Energy Optimization in Dynamic Environment

Increasing WSN Life Span and Packet Count through Hybrid Model and Clustered Network Algorithm

Abstract

Data collection from harsh environment is depend on sensor device network having wireless communication. Device working depend on battery life and energy dissipation depends on packet delivery in network. This paper has proposed a hybrid model to increase the energy utilization of nodes for WSN life enhancement. Due to dynamic nature of the work nodes clustering approach was used by the work. Wolf nature based genetic algorithm identifies cluster center nodes in WSN on the basis of available energy and distance from base station. Neural network was used by the model for classifying the nodes into fit and weak class. Neural network selected nodes were used in the wolf algorithm which ultimately cluster network. Experiment was on different combination of monitor area and number of nodes. Result shows that proposed has increases the life span of the model with high packet count as compared to other existing models of WSN energy optimization.