Energy Optimization for Green Communication in IoT Using Harris Hawks Optimization

Abstract

Internet of Things (IoT) has gained immense attention over the last decade. Even though IoT has great potential, several issues like security, energy optimization, data storage, real time data analytics, etc. have hindered the realization of true potential of IoT. As the sensors in the IoT network continuously monitor the environment, the longevity of the IoT networks is affected. Many researchers have tried to solve the issue of network longevity/energy optimization in IoT networks in the past few years. Optimizing the energy in IoT networks is still undergoing research. One of the methods used to optimize the energy in IoT networks is through selecting the optimal Cluster Head (CH) in the IoT networks. In this paper we have made an attempt to optimize the energy utilization in the IoT networks through an optimal CH selection using recently developed nature inspired algorithm, namely, Harris Hawks Optimization algorithm (HHO). The performance of the HHO-based CH model is analyzed through several metrics such as delay, load, number of alive nodes, residual energy, and temperature. The experimental results prove that the proposed HHO-based CH selection method performs better than the state-of-the-art CH selection models. The proposed model extends the network’s lifetime by keeping more alive nodes even after the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3000^{\textit {th}}$ </tex-math></inline-formula> round. The proposed model retains thirty nodes, genetic algorithm (GA) retains one node, artificial bee colony algorithm (ABC) retains three nodes, moth-flame optimization algorithm (MFO) retains seven nodes, and whale optimization algorithm (WOA) retains eleven nodes.