Minimizing the Energy Consumption and Exploiting the NLT by E2HCA Model in WSN

Abstract

In recent years, Wireless Sensor Network (WSN) has arisen as a practical option for many sectors needing smart technology. Despite its impressive credentials, Wireless Sensor Networks' excessive need for power remains a significant limitation. There is a pressing need to create a trustworthy Wireless Sensor Network with efficient energy and network lifetime due to the proliferation of tiny sensors with incomplete resources. One of the actual ways to deal with these matters is to divide the nodes into clusters. So, it is crucial to use existing energy efficiently to prevent energy waste. In this study, we suggest an Energy-Efficient Hybrid Clustering Algorithm to reduce the high-energy ingesting and increase the network lifetime of Wireless Sensor Networks. The position and speed of a gas molecule, among other Kinetic Gas Molecule Optimization particle characteristics, are initially determined by calculating their kinetic energy. Hybridization of Kinetic Gas Molecule Optimization arises from Kinetic Gas Molecule Optimization's faster convergence in space. Pelican Optimization Algorithm is used to fix the problems with Kinetic Gas Molecule Optimization by changing its inertia weight. Throughput the number of live/dead nodes are all validated against other prominent meta-heuristic methodologies in a MATLAB environment simulation of the proposed Kinetic Gas Molecule Optimization-Pelican Optimization Algorithm.