Find in Library

Effective research has been aimed at increasing the distributed compute dependent Software Define Network (SDN) with high-level Intelligent - Internet of Things (I-IoT). Wireless sensor networks come with a set of resource restrictions. Still, only a few functions are often configured such as energy restraint and the concerted demands that are vital for IoT application routing performance. A major technique for solving the expansion of network scalability by applying Mobile Sink (MS). The construction of data transmission optimal path, the detection of an optimal set data-gathering points <inline-formula> <tex-math notation="LaTeX">$O_{DG} $ </tex-math></inline-formula> and MS scheduled with dynamic networks for energy-efficient techniques, that the network&#x2019;s lifetime in enormous complications, principally in large-scale IoT networks. The research work proposes an Research Objective: i) Develop an energy-efficient routing technique for large-scale I-IoT networks within a cloud-based SDN system. ii) Optimize network scalability, lower-level routing, and load balancing using Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). The prime aim of cloud-based SDN with AI is to determine: a lower level routing in the perception layer, a load-balanced Cluster Table (CT), an optimal <inline-formula> <tex-math notation="LaTeX">$O_{DG} $ </tex-math></inline-formula> points, and MS optimal paths <inline-formula> <tex-math notation="LaTeX">$O_{MSpath} $ </tex-math></inline-formula>. The main contribution of proposed routing is i) Energy Minimization (EM): The proposed routing minimizes energy dissemination by the Cluster Head (CH) in critical conditions (EM-CH). ii) Enhanced Energy Balance (EEB): The EC-based SDN, considering both Optimal Data-Gathering (<inline-formula> <tex-math notation="LaTeX">$O_{DG}$ </tex-math></inline-formula>) and Mobile Sink (MS) advancements, achieves enhanced energy balance during network routing (EEB-SDN). Research results validate the proposed model stability that improves the network lifetime up to 63&#x0025;, the energy usage in the network is reduced up to 78&#x0025;, the high volume data loaded to the MS up to 95&#x0025;, and the delay of the <inline-formula> <tex-math notation="LaTeX">$O_{MSpath} $ </tex-math></inline-formula> by 69&#x0025; when compared with various model.