Energy-Efficient IoT Networks Using AI Driven Approaches

Abstract

Energy efficiency in IoT networks is increasingly becoming a critical issue as the number of interconnected devices continues to grow. This paper investigates AI-based strategies to improve energy efficiency within IoT networks. By employing machine learning techniques such as neural networks, decision trees, and Reinforcement learning (RL), we aim to forecast and optimize energy consumption trends. Our research involves gathering data from diverse IoT environments and assessing the effectiveness of these models under both simulated and real-life conditions. The findings indicate notable enhancements in energy consumption, leading to longer battery life, decreased operational expenses, and reduced environmental impact. These results emphasize the necessity of incorporating AI into IoT systems for the development of sustainable and efficient networks. The AI-driven approaches enable IoT devices to function more effectively, resulting in considerable energy savings and cost reductions. This paper adds to the expanding research on sustainable IoT solutions and illustrates AI's potential to tackle significant energy efficiency issues in this domain.