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).

物联网是最重大的技术突破之一，有望在未来实现更高水平的连接和控制。物联网网络持续快速扩展，物联网生态系统由网络中数百万个互连的自组织设备组成。资源的有效利用保证了服务质量的提高。一切都通过被称为物联网 (IoT) 的分布式系统连接到互联网。物联网基础设施中有大量网关和资源。由于网络异构性和物联网设备的多样性，资源分配（RA）具有挑战性；许多实用的方法、策略和实施方案正在被提出并用于解决 RA 问题 (RAP)。 IoT 资源分配和调度 (RAS) 性能在此类系统中至关重要，因为 RAS 将资源分配给开放网关并处理映射资源和网关。需要网关来连接物联网环境中的数百个资源。所提出的工作基于RAS问题，旨在通过使用大象群优化（EHO）算法来实现物联网中的最佳RA，以降低网关和资源之间的总通信成本。所提出的 EHO 算法已与其他已使用的算法进行了对比，结果表明所提出的算法的性能符合预期。所提出的解决方案在 TCC 和收敛速度方面优于粒子群优化 (PSO)、遗传算法 (GA)、蚁群优化 (ACO) 和灰狼优化 (GWO)。