Machine Learning Approaches for Energy Efficiency in IoT Networks

Abstract

The rapid proliferation of Internet of Things (IoT) devices has led to a surge in energy consumption, necessitating innovative strategies to enhance energy efficiency. Machine Learning (ML) has emerged as a promising approach to optimize energy utilization in IoT networks by enabling intelligent data processing, adaptive resource allocation, and predictive maintenance. This paper explores various ML techniques, including supervised, unsupervised, and reinforcement learning, for optimizing energy consumption in IoT ecosystems. It highlights key challenges such as data heterogeneity, real-time processing constraints, and limited computational resources while discussing potential solutions like federated learning, edge computing, and energy-aware neural networks. The study also presents recent advancements and case studies that demonstrate the effectiveness of ML-driven energy-efficient IoT frameworks. By integrating ML-based approaches, IoT networks can achieve enhanced sustainability, prolonged device lifespan, and improved operational efficiency. Future research directions focus on lightweight ML models, decentralized learning paradigms, and AI-driven energy harvesting techniques to further advance energy-efficient IoT networks.