Load Balancing in Region Based Clustering for Heterogeneous Environment in WSNs Using AI Techniques

Wireless Sensor Networks (WSNs) contains a large number of sensor nodes with restricted energy. The sensing and transmitting of data involves a huge amount of energy consumption. Therefore, Clustering is considered as one of the powerful approaches for efficient utilization of energy. The heterogeneous environment contains different types of sensor nodes in term of sensing, computation, communication and power. The proposed Load Balancing in Region Based Clustering approach is used to balance the load of super nodes regions by dividing the region of super nodes into levels and levels into clusters using inverted binary tree concept to optimize Cluster Head (CH) selection using Fuzzy Logic Techniques. The Unequal Region Based clustering approach is used to deploy different types of sensor nodes in different region to provide efficient utilization of coverage area. Hybrid routing is used for transmitting data to Base Station (BS). The protocol optimizes the number of CH selection and balance the load of CH. The lifetime of the network is improved by efficient utilization of energy.