Street Light Fault Detection System

Urban streetlight systems often rely on manual monitoring, leading to inefficiencies in maintenance, excessive energy consumption, and increased operational costs. Traditional methods do not provide real-time diagnostics, leading to a delayed notice of fault and extended downtime. To this end, this paper presents an IoT based smart streetlight monitoring system that aims at automated fault detection, along with enabling data based decision making. The system is built using ESP32 microcontroller interfaced with LDR sensors for light intensity measurement, DS3231 RTC module for accurate timestamp, and Neo-6M GPS module for accurate location tracking. Configurable thresholds enable determining the streetlight status from OFF, DIM, ON, or FLICKERING, which is then transmitted through Wi-Fi to a Firebase cloud database. Build a Real-time Interactive Dashboard Using Flask for Streetlights Conditions Monitoring and Energy Consumption Analysis (Automated Fault Alerts). This approach is shown via experimental results to benefit response time to lighting failures and energy savings through intelligent dimming strategies. It will be a scalable, cost-effective Intelligent framework for city lighting management solutions that is cost-effective for maintenance and has the potential to help achieve the vision of sustainable smart city development from the bottom up with a data-driven landscape.