john12356/streetlight-fault-detection-web-part-

The Street Light Fault Detection IoT project employs smart sensors on street lights, communicating data to a central server. A web application provides real-time monitoring and fault alerts to administrators, enhancing efficiency, reducing maintenance costs, and ensuring well-lit and safe urban spaces.

Street Light Fault Detetion - Web Part

This project focuses on leveraging Internet of Things (IoT) technology to monitor and detect faults in street lights, enhancing efficiency and reducing maintenance costs. The system involves the deployment of IoT devices on each street light, collecting real-time data and transmitting it to a central server.

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Table of Contents

• Introduction
• Features
• Technologies Used
• Installation
• Usage
• Contributing
• License

Introduction

The Street Light Fault Detection IoT project is a cutting-edge initiative designed to enhance the efficiency of urban lighting infrastructure. By deploying smart sensors on street lights and leveraging Internet of Things (IoT) technology, the system monitors real-time data to detect faults and anomalies. A user-friendly web application serves as a centralized platform, providing administrators with instant insights, alerts, and historical data, ultimately contributing to cost savings, improved public safety, and optimized urban lighting management.

Features

• Real-time Monitoring: Constantly monitors the status of street lights in real-time, providing up-to-the-minute information on their operational status.
• Fault Detection Algorithms: Utilizes sophisticated algorithms to analyze sensor data, promptly identifying faults such as bulb failures, power supply issues, or abnormal light intensity.
• Instant Alerts: Administrators receive immediate notifications through a web application, enabling swift response to faults and efficient maintenance.
• User-Friendly Web Application: A graphical interface allows administrators and maintenance teams to easily access and interpret data, promoting efficient decision-making.
• Cost Efficiency: Early fault detection minimizes maintenance costs by addressing issues promptly, contributing to overall cost efficiency.
• Improved Public Safety: Ensures well-lit streets by promptly addressing faults, enhancing public safety in urban environments.
• Energy Consumption Optimization: Analyzes data to optimize energy consumption by efficiently managing the operation of street lights.
• Scalability: Designed to scale, accommodating the integration of additional street lights and expanding coverage as urban infrastructure evolves.

Technologies Used

• React.js: A JavaScript library for building user interfaces.
• HTML5 & CSS3: Markup and styling languages for building web pages.
• JavaScript: A programming language for adding interactivity to web applications.
• NodeJS: Node.js is an open-source, cross-platform JavaScript runtime environment that allows developers to execute JavaScript code server-side.
• ExpressJS: Express.js is a minimal and flexible web application framework for Node.js.
• Gmaps : Google Maps is a web-based mapping service that provides interactive maps, real-time traffic information, and location-based services.

Installation

To run this portfolio locally, follow these steps: Clone the repository: git clone https://github.com/John12356/StreetLight-Fault-Detection-.git

Frontend

1. Navigate to the project directory: cd client
2. Install the dependencies: npm install
3. Start the development server: npm run dev
4. Open your browser and visit: http://localhost:5173

Backend

1. Navigate to the project directory: cd server
2. Install the dependencies: npm install
3. Create a .env file & add
   1. MONGO_URL = your mongodb url
   2. PORT = Any Port that you want to run your server (8080)
4. Start the development server: npm start
5. Open your browser and visit: http://localhost:8080

Usage

The Street Light Fault Detection IoT project optimizes urban lighting infrastructure by providing real-time monitoring, early fault detection, and data-driven insights. It enables efficient maintenance, cost savings, and enhanced public safety through proactive measures, remote monitoring, and scalable adaptability to evolving urban landscapes.

Contributing

Contributions are welcome! If you'd like to contribute to My Netflix clone, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix: git checkout -b my-feature
3. Commit your changes: git commit -m 'Add some feature'
4. Push to the branch: git push origin my-feature
5. Open a pull request.

License

This project is licensed under the MIT License.

Contact

For any queries [email protected]