Energy Harvesting in Cognitive Radio Networks using Local Search based Dingo Optimization Algorithm

Cognitive Radio Networks (CRNs) have been introduced as an empowering advancement which enables the dynamically unutilized spectrum to enhance the spectral effectiveness. Energy efficiency and the throughput are the apprehensions for the energy-harvesting CRN. Nevertheless, obtaining a greatest maximum level of these metrices needs the optimization of sensing duration, transmission time and the energy harvesting. To tackle these problems, this articles purposes to propose the Local Search based Dingo Optimization Algorithm (LS-DOA) approach for energy harvesting in CRN. The LS-DOA enhances an energy harvesting procedure through balancing an energy collected and obtained. This flexibility results in effective energy management as well as make sures that energy is significantly harvested from the obtainable ambient playforms. LS-DOA is robust in dynamic environments, regulating to changeable spectrum obtainability and different energy harvesting environments. This designs it optimal for CRNs, where network conditions are highly variable. The multi-objective functions such as energy efficiency and throughput are considered to estimate the model effectiveness. The experimental results shows that the proposed LS-DOA approach attains the average throughput and energy efficiency of 0.241 and 2.938 as compared to the existing approaches like DOA and Pelican Optimization Algorithm (POA).