An Adaptive Genetic Co-relation Node Optimization Routing for Wireless Sensor Network

Abstract

Wireless sensor network is designed with low energy, and limited data rates. In wireless sensor networks, the sensors are designed with limited energy rates and bandwidth rates. Maximizing the network lifetime is a key aspect in traditional Wireless communication to maximize the data rate in typical environments. The clustering is an effective topology control approach to organize efficient communication in traditional sensor network models. However, the hierarchical-based clustering approach consumes more energy rates for large-scale networks for data distribution and data gathering process, the selection of efficient cluster and cluster heads (CH) play an import role to achieve the goal. In this paper, we proposed an Adaptive Genetic Co-relation Node Optimization for selecting an optimal number of clusters with cluster heads based on the node status or fitness level. Using the tradition Genetic Algorithm, we achieved the Cluster head selection and the co-relation approach identifies the optimal clusters heads in a network for data distribution. Cluster head election is an important parameter, which leads to energy minimization, and it is implemented by Genetic Algorithm. Appropriate GAs operators such as reproduction, crossover and mutation are developed and tested.