IOT based smart street light fault detection management system
Department of Information Technology, Bharathiar University, Coimbatore, Tamil Nadu, India– 641046.
Review Article
World Journal of Advanced Research and Reviews, 2024, 21(03), 1659–1666
Article DOI: 10.30574/wjarr.2024.21.3.0685
Publication history:
Received on 19 January 2024; revised on 02 March 2024; accepted on 05 March 2024
Abstract:
In the contemporary landscape of urbanization and technological progress, efficient resource management, particularly in energy consumption, is paramount. Street lighting stands out as a significant energy consumer in urban environments. This paper proposes a Smart Street Light Fault Detection System utilizing Internet of Things (IoT) technology, a NodeMCU microcontroller, Light Dependent Resistor (LDR), street lights, and the ThingSpeak. The system employs the NodeMCU microcontroller to monitor ambient light conditions through the LDR sensor, enabling automatic control of street lights to optimize energy usage. Furthermore, it integrates fault detection mechanisms to identify abnormalities in street light operations, such as constant light levels amidst changing environmental conditions, indicative of potential faults. Through the ThingSpeak, users can remotely monitor real-time LDR readings, receive notifications about detected faults, and take necessary actions, empowering city authorities or maintenance personnel with timely information for addressing issues and ensuring smooth infrastructure operation. This smart system offers advantages like energy efficiency, automated control, and proactive fault detection, facilitating significant energy savings, reduced maintenance costs, and enhanced sustainability in urban environments, thereby showcasing a practical implementation of IoT technology for smarter and more sustainable cities.
Keywords:
NodeMUC esp8266; LDR; Relay; WiFi Module; ThingSpeak.
Full text article in PDF:
Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0