QoS based Optimal Path Selection Using Adaptive Sparse Bayesian Find-FixFinish-Exploit-Analyze Based Extreme Learning Machine for SDN based IoT Networks

Abstract

SDN (software defined network) is a networking approach which enables the IoT (internet of things) to be maintained through programming. The assessment of best routing path satisfying the requirements of QoS (quality of service) possess a significant role in networking. Several methods have been employed by the researchers for maintaining the necessities of QoS. But due to certain limitations like data latency, protocol complexity and bursty traffic which affects the network performance. In the proposed work, adaptive sparse bayesian-find fix finish exploit analyze-extreme learning machine (ASB-F3EA-ELM) is employed with four steps such as cluster formation, cluster head selection, rule caching policy and optimal path selection. The cluster formation is performed by using grid structures for forwarding the data packets. To avoid data transfer from all the IoT nodes, process of cluster head selection for each cluster is performed by modified aquila optimizer (MAO). A prominent rule caching policy is implemented for reducing rule caching cost in SDN based IoT architecture. The best routing path for effective data transfer is performed by the approach of ASB-F3EA-ELM. The performances of proposed approach are analyzed in the NS2 platform. The outcomes are estimated in terms of bandwidth, rule caching cost, delay, throughput, network lifetime and packet loss rate. When comparing to the existing methods, the proposed work shows better output in satisfying the requirements of QoS as it achieved improvement in throughput of 3.89% to 7.97%, packet loss rate reduced from 0.11% to 0.07%, network lifetime increased by 13.75% to 27.5% as compared to PSO, GA, ACO and GWO methods.