IoT Based Street Light Fault Detection and Location Tracking

Abstract

Abstract: In the era of smart cities, efficient management of public infrastructure is paramount. Street lighting is a fundamental component of urban infrastructure, ensuring safety and security for citizens. However, the conventional methods of monitoring street lights are often inefficient and labor-intensive, leading to delayed detection and resolution of faults. This paper proposes a novel approach for street light fault detection and location tracking leveraging advanced technologies such as Internet of Things (IoT), machine learning, and geographic information systems (GIS). The proposed system consists of a network of IoT-enabled sensors installed on street lights, capable of monitoring various parameters such as luminosity, power consumption, and operational status in real-time, voltage drop, current loss. Through machine learning algorithms, the system intelligently analyzes the sensor data to detect anomalies and potential faults in the street lights. Upon detection of a fault, the system employs location tracking techniques, to precisely pinpoint the faulty street light's location