Hybridization of Machine Learning Techniques for WSN Optimal Cluster Head Selection

Wireless sensor networks (WSN) keep developing in recent days concerning the self-covered network, self-healing and association of system component circuit selections that enable implementation process. network lifetime stabilization is essential to providing a higher quality experience consumers. The wireless associated with classifiers learning data pattern further modify cluster selection produce dynamic results. presented focused on creating an efficient which head dynamically programmed improve life span devices. energy utilized by each node pre-programmed random assignments. values are configured machine techniques hop death. models developed using parameters help project consumption. proposed considers support vector (SVM), Gaussian regression process (GRP) enabled comparative study lifespan analysis systems make efficient. model used test current heads rectangle model. Evaluation flexibility obtained selection.