Energy Efficient Optimized Routing Technique With Distributed SDN-AI to Large Scale I-IoT Networks
P K Udayaprasad, J Shreyas, N. N. Srinidhi, S. M. Dilip Kumar, P. Dayananda, Sameh Askar, Mohamed Abouhawwash
Abstract
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 ODG and MS scheduled with dynamic networks for energy-efficient techniques, that the network’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 ODG points, and MS optimal paths OMSpath. 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 (ODG) 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%, the energy usage in the network is reduced up to 78%, the high volume data loaded to the MS up to 95%, and the delay of the OMSpath by 69% when compared with various model.