Meta-heuristic Artificial Humming Bird Algorithm Based Energy Efficient Cluster Head Selection (MAHA-EECHS) in Wireless Sensor Networks

Abstract

A wireless sensor network (WSN) improves wireless communication by using hundreds or thousands of nodes to gather data. The lifespan of the nodes and balanced energy consumption are the major issues in the WSN. Long-term WSN efficiency requires optimising node energy. Selecting the optimal node as the cluster head improves energy usage in wireless sensor networks. The Artificial Hummingbird algorithm is used in this paper to identify the best cluster head selection in homogenous wireless sensor networks. The proposed algorithm's innovation lies in the fact that it takes into account a number of parameters like residual energy, intra-cluster distance, and balanced cluster formation while choosing a CH from a homogeneous sensor network. The performance analysis of the proposed algorithm considers four parameters: average energy consumption, total energy consumption, first node death, and residual energy. When compared to other algorithms, MATLAB-based simulation analyses show that the proposed algorithm MAHA-EECHS outperforms them.