Energy-Efficient IoT Networks: Optimizing Resource Management through Machine Learning

Abstract

The rapid expansion of Internet of Things (IoT) devices has brought out novel prospects and obstacles, namely in the domains of energy management and resource optimisation. Ensuring energy efficiency becomes essential as IoT networks grow in order to promote sustainable deployments, save operating costs, and extend the life of devices. The increasing intricacy and dynamic character of contemporary IoT systems sometimes prove to be too much for traditional resource management techniques to handle. The potential of machine learning (ML) to improve resource management in energy-efficient Internet of Things networks is examined in this research. Through the use of machine learning techniques, such as deep learning, supervised learning, and reinforcement learning, Internet of Things systems are able to forecast network traffic, allocate resources intelligently, and reduce energy usage in real time. The study examines cutting-edge methods, addresses important issues, and indicates areas for further investigation. In the end, this research highlights how machine learning may play a revolutionary role in creating IoT networks that are scalable, energy-efficient, sustainable, and able to satisfy the expectations of a world that is becoming more and more connected.