TOWARDS GREEN WIFI NETWORKS AN ML AND AI-BASED FRAMEWORK FOR ENERGY EFFICIENCY OPTIMIZATION

Abstract

The increasing demand for wireless connectivity has led to a significant rise in energy consumption by WiFi devices, necessitating the development of efficient energy management strategies. This paper presents a novel framework that leverages machine learning (ML) and artificial intelligence (AI) techniques to optimize the energy efficiency of WiFi devices without compromising network performance. The proposed approach utilizes predictive models to analyze historical data on device usage and network activity, allowing for the identification of energy-saving opportunities. In addition, AI-driven algorithms are used to adapt to changing environmental conditions and user behaviors, enabling real-time optimization of energy consumption. The framework additionally integrates predictive maintenance techniques to proactively identify and address energy inefficiencies and hardware issues. Experimental results show significant improvements in energy efficiency, with energy consumption reduced by up to 30% compared to traditional approaches. The findings emphasize the potential of ML and AI in improving the sustainability and cost-effectiveness of WiFi networks, opening up opportunities for future research and development in this field. Keywords: Energy efficiency optimization, Machine learning in WiFi networks, Artificial intelligence for energy management, Predictive maintenance for WiFi devices, Sustainability in wireless networks.