Abstract

Urban lighting systems are essential for safety, security, and quality of life, but they often consume significant energy and lack adaptability to changing conditions. Traditional lighting systems rely on fixed schedules and manual adjustments, leading to inefficiencies such as over-illumination and energy waste. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban lighting by enabling real-time adjustments, energy savings, and adaptive illumination based on environmental conditions and human activity. By integrating data from motion sensors, weather forecasts, and traffic systems, cities can reduce energy consumption, enhance safety, and improve the quality of life for residents. Experimental results demonstrate significant improvements in energy efficiency, lighting quality, and operational costs, offering a sustainable blueprint for smart urban lighting systems.