Energy Efficiency in IoT Using Nature-Inspired Algorithms

Abstract

This research explores the convergence of energy efficiency and Internet of Things (IoT) systems by utilizing nature-inspired algorithms. With the increasing number of IoT devices, it is crucial to priorities energy efficiency in order to achieve sustainable and cost-effective deployment. This study seeks to develop intelligent and adaptable solutions to improve energy efficiency in IoT networks by applying principles derived from nature- inspired algorithms, including genetic algorithms, particle swarm optimisation, and ant colony optimisation. The suggested algorithms aim to optimise device communication, data processing, and system operation by imitating the self-organizing and adaptive behaviors found in natural systems. This optimisation leads to reduced energy usage. The abstract emphasises the use of nature-inspired algorithms in IoT frameworks to tackle the urgent issue of energy efficiency, promoting a more sustainable and resilient future for interconnected smart systems.