IoT and ML for Air Pollution Monitoring with Real Time Data and Prediction of Health Advisory
Objective
The main objective is to develop an IoT and ML-based system for real-time air pollution monitoring using sensors at traffic signals to track CO, CO?, and smoke levels. It analyzes data with a Decision Tree Classifier to provide health advisories, safe travel routes, and personalized recommendations via a mobile app.
Abstract
This project presents an IoT-based air pollution monitoring and health advisory system using Arduino Mega, MQ135, MQ2, DHT11, PMS5003 sensors, LCD display, and buzzer. The system continuously monitors air quality, harmful gases, temperature, humidity, and particulate matter levels in real time. A Random Forest machine learning algorithm analyzes the collected data to classify pollution levels and predict health advisories. The air quality status is displayed on the LCD screen, and the buzzer generates alerts when pollution levels exceed safe limits. The proposed system provides a low-cost, intelligent, and efficient solution for environmental monitoring and public health protection.
Keywords: Arduino Mega, Air Pollution Monitoring, Random Forest, IoT, PMS5003.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Specifications
Hardware components:
- Arduino Mega
- MQ135 Sensor
- MQ2 Sensor
- PMS 5003
- USB web camera
- Buzzer
- DHT11 Sensor
- LCD
- Connectors-10.
Software requirements:
- Arduino IDE
- Embedded C
Learning Outcomes
- Arduino pin diagram and architecture
- How to install Arduino UNO / setup software
- Setting up and installation procedure for Arduino UNO
- Introduction to Arduino UNO environment / development setup Basic programming in Arduino UNO (Embedded C)
- Basics of Embedded programming using Arduino UNO
- Basics of IoT platforms
- Working of power supply
- About Project Development Life Cycle:
- Planning and Requirement Gathering (software, tools, hardware components, etc.)
- Schematic preparation
- Code development and debugging
- Hardware development and debugging
- Development of the project and output testing
- Practical exposure to:
- Hardware and software tools
- Solution providing for real-time problems
- Working with team/individual
- Work on creative ideas
- Skills developed:
- Project development skills
- Problem analyzing skills
- Problem solving skills
- Creativity and imaginative skills
- Programming skills
- Deployment
- Testing skills
- Debugging skills
- Project presentation skills
- Thesis writing skills
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
