Smart Street Light System: An IoT-Enabled Light System for Smart City Application

Abstract

Introduction: The Smart Street Light System utilizing Internet of Things (IoT) technology is an innovative solution designed to optimize urban lighting management. By leveraging sensors and IoT devices, the system dynamically adjusts street lighting based on ambient light levels and pedestrian movement. Traditional street lighting systems suffer from inefficiencies such as unnecessary energy consumption and high maintenance costs. The integration of real-time environmental data and automation in street lighting addresses these challenges, improving both energy efficiency and public safety.

Objectives: This research aims to develop an IoT-enabled Smart Street Light System that enhances energy efficiency, minimizes maintenance costs, and improves urban safety. The system integrates Light Dependent Resistors (LDRs) and motion sensors to ensure that streetlights operate at optimal brightness only when required. Additionally, the project seeks to explore the scalability and sustainability of the system in both rural and urban environments, adapting lighting control mechanisms based on real-time data analysis.

Methods: The proposed methodology involves the deployment of an Arduino Uno-based control system equipped with LDRs, ultrasonic sensors, and LED streetlights. The system dynamically adjusts brightness levels by detecting object movement and speed, distinguishing between pedestrians and vehicles to optimize energy usage. In Future Data collected from sensors is transmitted to a central monitoring system for real-time tracking and predictive maintenance. The study also incorporates modular variations for rural and urban areas, integrating different sensor types and environmental data sources such as Weather API and Sunrise-Sunset API for enhanced adaptability.

Results: The Smart Street Light System demonstrated a significant reduction in energy consumption, with LDR-based brightness adjustment leading to energy savings of up to 40%. The integration of motion detection enhanced public safety by ensuring adequate illumination during critical times. Additionally, predictive maintenance mechanisms improved system reliability by reducing downtime and maintenance costs. The system’s ability to dynamically adjust brightness based on real-time data supports energy-efficient urban infrastructure development.