Machine Learning Techniques for Energy-Efficient Routing in IoT Communication Networks

Abstract

The rapid proliferation of the Internet of Things (IoT) has transformed modern communication infrastructures by enabling seamless connectivity among heterogeneous smart devices, sensors, and autonomous systems. However, the limited energy resources of IoT devices and the increasing demand for scalable, reliable, and low-latency communication have created significant challenges for routing and network sustainability. Traditional routing protocols often struggle to optimize energy consumption while maintaining network performance in dynamic and resource-constrained IoT environments. Machine Learning (ML) techniques have emerged as promising solutions for enabling intelligent, adaptive, and energy-efficient routing in IoT communication networks through predictive analysis, autonomous decision-making, and real-time optimization. This study investigates the role of machine learning techniques, including supervised learning, unsupervised learning, reinforcement learning, deep learning, and hybrid optimization models, in enhancing routing efficiency and minimizing energy consumption in IoT communication networks. The research examines how ML-based routing frameworks improve packet delivery ratio, reduce communication overhead, optimize path selection, prolong network lifetime, and enhance Quality of Service (QoS) in heterogeneous IoT ecosystems. Furthermore, the study explores major challenges affecting ML-enabled energy-efficient routing, including resource limitations, computational complexity, security vulnerabilities, scalability issues, data heterogeneity, and model adaptability in dynamic networking environments. By analyzing recent advancements and intelligent routing mechanisms, the paper highlights the transformative potential of machine learning in developing sustainable, autonomous, and energy-aware IoT communication infrastructures. The findings indicate that ML-driven routing techniques significantly improve energy efficiency, network reliability, and communication performance, thereby supporting the development of scalable smart city applications, industrial automation, healthcare systems, and intelligent environmental monitoring networks.