An Enhanced Sector Low Energy Adaptive Clustering Hierarchy (S-LEACH) using Modified Grey Wolf Optimisation Algorithm and Game Theory

Abstract

Wireless Sensor Networks (WSN) and other sensing and communication technologies have given man the ability to keep tabs on his  surroundings. Distributed sensors in the form of WSNs are used to keep tabs on environmental or physics-related variables. These  sensors operate together to send information via the internet to a central location, where it may be used to inform decisions that have real consequences for people's lives. As detecting, processing, and transmitting all take a lot of energy, WSNs are powered by batteries  that cannot be replaced during data transmission. Therefore, it is essential to increase the network's durability by reducing the energy  consumption of each sensor node. Power consumption problems exist in network's communication routes between the sensor nodes  might have disastrous effects on a network that relies on timely data transmission. Since the sensor nodes need electricity to function,  losing that power disrupts data transmission and, in most situations, kills the network. A serious issue with WSNs is their rapid loss of  energy. Studies have shown that switching the node from active to sleep mode while it is not in use may extend the lifespan of WSNs.  Some have argued that a mobile charger should be made available instead of a sensor node with a changeable battery. Even though  energy harvesting the practice of drawing power from the surroundings and transforming it into electrical energy has the potential to  address this issue, there are situations in which this is not possible. The objective of this study is to design a more energyefficient  algorithm for usage by sensor nodes in order to decrease their power consumption. By combining game theory and Grey Wolf  Optimisation with the existing Sector Low Energy Adaptive Clustering Hierarchy (LEACH) routing system, a more efficient and effective  algorithm was developed called Enhance Game and Grey Wolf Algorithm (EG-GWA).