IndoorAirQualityEE5003

Intelligent Indoor Air Quality and Ventilation Management in Confined Spaces using IoT

Problem Statement

• In recent years, energy-efficient homes have become more airtight, leading to decreased ventilation and potential health concerns.
• We don’t know at what time CO2 levels are high
• Everyone cannot operate windows/ventilators often to allow fresh air
• No access to data all the time with and without internet

Main Objective of Project

• Implement an air quality and temperature monitoring system using Raspberry Pi and an Adafruit SCD40 Sensor (CO2, humidity, and temperature).
• Automate responsive actions such as vent control using a servo motor to optimise the indoor environment.
• Provide timely alerts to notify individuals of any potential air quality issues.
• Automate generation of air quality reports in Raspberry Pi (Provided results like how often it's required to allow fresh air or operate ventilation)

Added Objectives while development

• To add server functionality to the Raspberry Pi and expose APIs to get the data in different formats (JSON, CSV)
• To develop a reliable Android mobile application that shows recent and historical data from ThingSpeak Cloud and Raspberry Sever to continuously provide users with sensor data information.
• To test the project with an additional DHT11 sensor to make a comparative analysis of the performance

Information about git repository

The entire project code was implemented using different branches, and after each task was completed, the related branch was merged into the master-integration branch. And at final stage that was merged into main branch, so all the latest code can be found in main branch.

To retain the old changes, No branches were deleted from starting

All Branches in the repository


Project Board

Whole project was implemented by following below steps:

• Created a task in the project board and defined what the task is, and updated the progress of the task promptly.
• Created a separate branch from the master-integration branch for each valuable feature.
• Worked on that branch, and when completed, all the changes were merged into the master-integration branch

So each branch will have some additional features or changes right from starting. And all the changes can be seen in the main branch together.

 Another View


Project Codes

• [Project Master Code]() This folder contains the final code of the hardware project [ProjectMasterCode.py](<Project Master Code/ProjectMasterCode.py>), which need to be executed on a Raspberry Pi.

• Reports generation folder information is detailed in this file, click to navigate -> ReportsREADME.md

• SCD40 folder contains all the codes in a SCD40/code folder and some initial test analysis and results.

SCD40/code folder contains below codes:

Each feature was kept added to the previous code and the the final evolved code is scd40ThingSpeakLEDServoCSVServerEmail and [ProjectMasterCode.py](<Project Master Code/ProjectMasterCode.py>) is the final code that needs to be used in Raspberry Pi

• scd40ThingSpeakLEDServoCSVServerEmail Latest code
• scd40ThingSpeakLEDServoCSVServerDHT11
• scd40ThingSpeakLEDServoCSVServer
• scd40ThingSpeakLEDServoCSV
• scd40ThingSpeakLEDServo
• scd40ThingSpeakLEDDHT11
• scd40ThingSpeakLED
• scd40ThingSpeak
• adafruitscd40 initial code

Android Application Project

AirQualityMonitor Android Application <- committed to a folder

This was the Android application developed; this project needs to be opened with android studio, and then all the gradle files will get automatically synced and then we can build and run the project.