Embedded CNN-Based AI System for Real-Time Air Quality Monitoring and Prediction in Smart Cities
Objective
To design and implement an embedded CNN-based AI system that continuously monitors real-time air quality parameters using environmental sensors and processes the data locally for efficient analysis in smart city environments. To develop a predictive model using convolutional neural networks (CNN) that accurately classifies pollution levels and forecasts future air quality trends, enabling timely alerts and data-driven urban environmental management.
Abstract
The Embedded CNN-Based AI System for Air Quality Monitoring and Prediction in Smart Cities is developed to monitor environmental pollution levels and predict air quality conditions using Artificial Intelligence and embedded computing technology. The proposed system utilizes multiple gas and environmental sensors including PMS5003 for particulate matter measurement, MQ-135 and MQ-2 sensors for harmful gas detection, and a DHT11 sensor for temperature and humidity monitoring. A Raspberry Pi serves as the central processing unit to collect and process sensor data. A Convolutional Neural Network (CNN) model is implemented to analyze environmental parameters and predict air quality conditions based on sensor readings. The monitored air quality parameters are displayed through an LCD module for local observation. Whenever abnormal pollution levels are detected, a buzzer alert is activated to provide immediate warning. This intelligent system enables accurate pollution monitoring and predictive analysis, supporting smart city environments by improving environmental awareness, public safety, and pollution management through AI-based embedded solutions.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Hardware components:
· Raspberry Pi
· Memory Card
· Power Supply
· Adapter
· PMS5003
· MQ-135 Sensor
· MQ-2 Sensor
· DHT11
· LCD
· Buzzer
Software requirements:
· Raspbian OS
· Python
Learning Outcomes
Learning Outcomes
- Understanding Raspberry Pi architecture and pin configuration
- Installation and setup of Raspberry Pi OS
- Software installation and system configuration for Raspberry Pi
- Introduction to Raspberry Pi development environment
- Basic programming using Python for embedded applications
- Fundamentals of Embedded Systems programming
- Basics of IoT platforms and cloud connectivity
- Understanding power supply and hardware interfacing
- Knowledge of sensor interfacing with Raspberry Pi
Project Development Life Cycle
- Planning and Requirement Gathering (hardware, software, and tools)
- Circuit and schematic preparation
- Program development and debugging
- Hardware interfacing and troubleshooting
- System integration and output testing
Practical Exposure
- Working with hardware and software tools
- Developing solutions for practical monitoring systems
- Individual and team-based project implementation
- Implementation of innovative and creative ideas
Skills Developed
- Embedded system development
- Problem analysis
- Problem solving
- Programming skills
- Creativity and innovation
- System deployment
- Testing and validation
- Debugging techniques
- Project presentation
- Technical documentation and thesis writing
Paper Publishing
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