How can a hybrid quantum-inspired gravitational search algorithm decrease energy consumption in IoT-based software-defined networks?

Abstract

The growth of Internet of Things (IoT) devices has prompted the growing use of software-defined networks (SDNs) in today's quickly changing technological environment. In SDN, execution and security of supporting applications and creating an adaptable network design allow the network to associate with applications legitimately. As a result, SDN promotes the growth of IoT-enabled devices, boosts network resource-sharing effectiveness, and boosts the reliability of IoT services. While these interconnected systems offer unprecedented convenience and efficiency, they also come with an increasing energy consumption challenge. The original features of these networks, such as the dynamic topology and energy constraints, challenge the routing issue in these networks. This article delves into the strategies and innovations that can effectively decrease energy consumption in IoT-based SDNs. The previous methods had some problems, such as increasing energy consumption, delay and network lifetime, etc. Thus, fuzzy and meta-heuristic methods have been used to maximize the search space and achieve optimum results. Due to the NP-hard nature of this issue, the Binary Quantum-Inspired Gravitational Search Algorithm (BQIGSA) is used in this paper to offer a fuzzy-based routing approach in IoT-based SDN, which aims to optimize energy, delay, and expected transmission rate. Fuzzy modeling, and particularly fuzzy routing algorithms, are explained in this study in relation to the decision-making component. The synergy of Fuzzy Logic and BQIGSA offers a promising avenue for enhancing IoT-based SDNs. This innovative approach tackles the challenges of uncertainty, energy optimization, and adaptive decision-making that are inherent in IoT networks. The simulation is performed through MATLAB. The outcomes of simulations and tests demonstrated that the suggested approach performed better than the current methods in terms of energy usage, delay rate, and data delivery rate.