Abstract

Street lighting plays a crucial role in ensuring public safety, urban aesthetics, and energy efficiency. However, manual monitoring and maintenance of street lights are time-consuming, inefficient, and prone to delays in fault detection. This paper presents an automated street light fault detection system designed to identify and report faults in real time. The proposed system utilizes sensors, microcontrollers, and wireless communication modules (such as IoT-based platforms) to monitor parameters like current, voltage, and illumination intensity. When an anomaly—such as lamp failure, wiring fault, or abnormal power consumption—is detected, the system automatically sends notifications to a centralized monitoring unit or maintenance personnel. The integration of machine learning algorithms or threshold-based decision models further enhances accuracy by distinguishing genuine faults from transient fluctuations. Experimental results demonstrate that the system significantly reduces maintenance time, operational costs, and energy wastage, while improving reliability and sustainability of street lighting infrastructure. This work contributes to the development of smart city initiatives by enabling autonomous, efficient, and cost-effective street light management.