Air Quality (HVAC) Monitoring Web App

This repository contains the updated version of website created to display the content of real-time HVAC (Heat, ventilation, and air conditioning) data monitoring.

Description

• This project was developed as part of the (BCSE313L) Fog and Edge Computing course at VIT-Chennai.
• The website serves as a responsive dashboard, offering visual representations of both historical and real-time data collected from various sensors and hardware components.
• As a pre-requisite for the project in the subject, this project adheres to the C2F2T (Cloud-to-Fog-to-Things and its reverse) model, as explained in the subsequent section.

Table of Contents

• Air Quality (HVAC) Monitoring Web App
  • Description
  • Table of Contents
  • Features
  • Tech-Stack
  • Project Overview
  • C2F2T Architecture
  • Links
  • License
  • Contact

Features

1. User-Friendly Web Interface 🌐
   Provides a fully functional, responsive and interactive website, featuring regular updates and a comprehensive view of air quality data anywhere.

2. Real-time Monitoring ⏳
   Continuously tracks air quality levels, providing instant data updates for timely analysis and response.

3. Low Latency Operations ⚡
   Ensures minimal delay in data processing and visualization, allowing for accurate real-time insights and decisions.

4. Emergency Alert System ⚠️
   Automatically sends immediate alerts for critical air quality levels, including fire or gas leakage detection, ensuring rapid response to potential hazards.

5. Comprehensive Data Collection 📊
   Utilizes a variety of sensors to gather diverse and comprehensive environmental data. Setup shown in the hardware setup section.

6. Interactive Data Visualization 📈
   Presents data in a visually appealing, interactive and easy-to-understand format, enabling users to interpret and analyze information effectively.

7. Robust Data Storage ☁️
   Utilizes Firebase for reliable and scalable NoSQL database storage, ensuring data integrity and accessibility. Enabling robust data management and retrieval.

Tech-Stack

• Python
• HTML
• CSS
• JavaScript
• Firebase

Project Overview

• Data Collection:

  • Data is collected by using various sensors such as MQ-series sensors, DHT-11, and flame sensors.
  • An Arduino periodically reads the data from these sensors.

• Data Passage:

  • Data collected from the sensors by Arduino is passed to the Raspberry-Pi using serial communication at appropriate baud-rate.
  • 

• Data Filtering and Display:

  • The Raspberry Pi splits, filters, and processes the received data locally.

  • Weather predictions are fetched using an API for the day and night at the specified location.

  • Based on the latest locally received data and online predictions, display graphics are generated and updated on an the LCD-TFT display.

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  • 

  • Further, the data is passed to the cloud for storage and further access.

• Data Storage:

  • Firebase, a NoSQL database, is utilized to create, retrieve, and update data.
  • The data received in this series is stored under specific firebase nodes.

• Web Interface:

  • A fully functional and responsive website is created and deployed on vercel.
  • The website fetches data from the cloud, and its components are updated periodically.
  • The website also features an Emergency Alert System, which can be a lifesaver in cases of fire or gas leakage in the monitored area.

• Hardware Setup:
  

  • 

C2F2T Architecture

1. Cloud-to-Things:

   • This aspect involves the flow of data and services from the cloud to the edge devices or "things" (such as sensors, actuators, or IoT devices).

2. Things-to-Cloud:

   • In contrast to C2T, T2C refers to the flow of data and services from the edge devices or "things" to the cloud.

3. Bidirectional Communication:

   • The C2F2T model emphasizes bidirectional communication between the cloud and edge devices, enabling seamless interaction and data exchange in both directions.
   • This approach benefits from various hardware computing power at different nodes in the IoT ecosystem.
   • Bidirectional communication enables real-time monitoring, control, and decision-making capabilities at the edge while leveraging the extensive computational and storage capabilities of the cloud.

4. In this project:
              

   1. Things:
      • All sensors act as things.
      • Things collect the data on ground level.
   2. Edge:
      • The edge device is an Arduino, which has limited computing power and basic computer functionalities.
      • It collects and temporarily stores the data within its limited small storage capabilities.
   3. Fog:
      • A Raspberry Pi is the middle device in the project. It gets data from the edge level, filters, and processes it with its relatively large compute power.
      • The RasPi thus acts as the fog layer.
   4. Cloud:
      • Finally, data is collected in the cloud.
      • This data is then used to serve the website.
      • The cloud can also be utilized to run predictive models and gain meaningful insights from the data.
      • Thus, leverages the power of machine learning and the resource-intensive nature of cloud infrastructure.

Links

1. Visit the deployed project on Vercel:
   

2. Video demonstration of project implementation:
   

License

Contact

Email	bhushanbsongire@gmail.com
LinkedIn	bhushan-songire