Green Networking: Energy-Efficient Routing Algorithms for Sustainable Networking

Abstract

The exponential growth of network traffic and the proliferation of Internet of Things (IoT) devices have significantly increased energy consumption in modern communication networks, contributing to rising carbon emissions and operational costs. This paper presents a comprehensive analysis of energy-efficient routing algorithms designed to optimize power consumption while maintaining quality of service in sustainable networking infrastructures. Through systematic evaluation of contemporary green routing protocols, including tree-based, fuzzy logic-enhanced, and machine learning-driven approaches, we demonstrate that optimized energy-aware routing can achieve up to 40% reduction in power consumption compared to traditional routing methods. Our analysis employs established datasets including NSL-KDD and UNSW-NB15, and utilizes simulation frameworks NS-3 and OMNeT++ for performance validation. The results indicate that hybrid optimization algorithms combining particle swarm optimization with fuzzy clustering show superior performance in balancing energy efficiency with network reliability. This research contributes to the development of sustainable networking solutions essential for reducing the carbon footprint of information and communication technology infrastructure.