Air Quality (HVAC) Monitoring Web App

This repository contains the 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
  • Team-mates
  • License
  • Contributions
    • Contact

Features

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

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

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

4. Comprehensive Data Collection
   Utilizes a variety of sensors to gather diverse and comprehensive environmental data.

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

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

   

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.

             

        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:
   Vercel Deployment Link

2. Clone the repository:

   git clone https://github.com/Bbs1412/air-quality-monitoring-system
   
   cd air-quality-monitoring-system

3. Preliminary Repository:
   Link to repository

Team-mates

1. Bhushan Songire (LinkedIn)
2. Vedant Choudhari
3. Ujjawal Kumar

Contributions

Any contributions or suggestions are welcome!

License

Contact

• Email - [email protected]
• Git - Bbs1412