The IoT resource allocation and scheduling using Elephant Herding Optimization (EHO-RAS) in IoT environment

IoT is one of the most significant technological breakthroughs and promises a higher level of connection and control in the future. The IoT network continues to expand rapidly, and the IoT ecosystem comprises millions of interconnected ad hoc devices across the network. Effective resource utilization guarantees the improvement of service quality. Everything is connected to the Internet through the distribution system known as the Internet of Things (IoT). Plenty of gateways and resources are in IoT infrastructure. Resource allocation (RA) is challenging due to network heterogeneity and the diversity of IoT devices; numerous practical approaches, strategies, and implementations are being presented and employed to resolve the RA problem (RAP). IoT resource allocation and scheduling (RAS) performance is essential in such a system since RAS allocates resources to open gateways and handles mapping resources and gateways. A gateway is needed to connect to hundreds of resources in the IoT environment. The proposed work is based on the RAS problem and aims to achieve optimal RA in the IoT by using the Elephant Herding Optimization (EHO) algorithm to lower the total Communication Cost between gateways and resources. The proposed EHO algorithm has been contrasted with others already in use, and the results show that the suggested algorithm performs as expected. The proposed solution is superior to others regarding TCC and Convergence rate than Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Gray Wolf Optimization (GWO).