https://github.com/bbs1412/air-quality-updated
This repository contains the updated website created to display the content of real-time HVAC (Heat, ventilation, and air conditioning) data monitoring. [ Second Version of air-quality-monitoring-system]
https://github.com/bbs1412/air-quality-updated
air-quality air-quality-monitor arduino firebsae fog-computing hvac raspberry-pi weather-app
Last synced: 2 months ago
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This repository contains the updated website created to display the content of real-time HVAC (Heat, ventilation, and air conditioning) data monitoring. [ Second Version of air-quality-monitoring-system]
- Host: GitHub
- URL: https://github.com/bbs1412/air-quality-updated
- Owner: Bbs1412
- License: gpl-3.0
- Created: 2025-02-14T06:10:03.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-02-22T10:31:20.000Z (2 months ago)
- Last Synced: 2025-02-22T11:26:06.592Z (2 months ago)
- Topics: air-quality, air-quality-monitor, arduino, firebsae, fog-computing, hvac, raspberry-pi, weather-app
- Language: JavaScript
- Homepage: https://air-quality-monitor-bs.vercel.app/
- Size: 8.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# `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](#c2f2t-architecture).
## Table of Contents
- [`Air Quality` (HVAC) Monitoring Web App](#air-quality-hvac-monitoring-web-app)
- [Description](#description)
- [Table of Contents](#table-of-contents)
- [Features](#features)
- [Tech-Stack](#tech-stack)
- [Project Overview](#project-overview)
- [C2F2T Architecture](#c2f2t-architecture)
- [Links](#links)
- [License](#license)
- [Contact](#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.
1. **Real-time Monitoring** ⏳
Continuously tracks air quality levels, providing instant data updates for timely analysis and response.
1. **Low Latency Operations** ⚡
Ensures minimal delay in data processing and visualization, allowing for accurate real-time insights and decisions.
1. **Emergency Alert System** ⚠️
Automatically sends immediate alerts for critical air quality levels, including fire or gas leakage detection, ensuring rapid response to potential hazards.
1. **Comprehensive Data Collection** 📊
Utilizes a variety of sensors to gather diverse and comprehensive environmental data. Setup shown in the [hardware setup section](#hardware-image).
1. **Interactive Data Visualization** 📈
Presents data in a visually appealing, interactive and easy-to-understand format, enabling users to interpret and analyze information effectively.
1. **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.
+ 
+ 
+ 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.
## 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).
1. 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.
1. 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.
1. 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:
[](https://air-quality-monitor-bs.vercel.app/)
1. Video demonstration of project implementation:
[](https://youtu.be/avSJYaPSGzs)
## License
[](https://www.gnu.org/licenses/gpl-3.0)
## Contact
|||
| - | - |
| **Email** | [[email protected]](mailto:[email protected]) |
| **LinkedIn** | [bhushan-songire](https://www.linkedin.com/in/bhushan-songire/) |
