Artificial Butterfly Optimization based Cluster Head Selection with Energy Efficient Data Aggregation model for Heterogeneous WSN Environment

The WSN is a new and urgent technology with many potential uses, including but not limited to health security, environmental monitoring, etc. Due to lower battery capacity, WSN has a restricted-energy resource. In order to solve the issue of unequal energy consumption among nodes, it is necessary to choose a sensor node from a cluster with more than enough power to make up for the weaker nodes. This paper develops the idea of heterogeneous WSN (H-WSN), which provides supplementary energy to the heterogeneity network. One method that has shown promise in overcoming this difficulty is the clustering technique, which optimizes energy consumption and extends the useful life of a sensor network. Even if the existing approaches function well, the computational complexity may rise due to the usage of a single mobile sink in their studies. As an alternative to communication among each CH and sink through a separate hop, the network uses Multiple Mobile Sinks (MMSs). The combination of the data collection and aggregation mechanism (DCA) and artificial butterfly optimization (ABO) based on CH selection allows for energy-efficient data transfer using MMSs in H-WSN. The CH assortment uses the distance parameter, remaining energy, and regular energy for the suggested energy efficiency model. The NS2 platform hosts the final product of the projected H- WSN. The suggested ABO-CH-DCA approach is superior to the baseline protocols in simulations on various measures, including throughput, network lifespan, remaining energy, dead nodes, and live nodes.