e19-co326-air-quality-management-system

Air Quality Management System

Team

E/19/133, HARISHANTH A., email
E/19/134, HARNAN M., email
E/19/137, HAYANAN T., email
E/19/142, ILLANGARATHNE Y.M.H.V., email
E/19/155, JAYARATHNA B.R.U.K., email
E/19/163, JAYASUNDARA J.M.E.G., email
E/19/166, JAYATHUNGA W.W.K., email
E/19/167, JAYAWARDENA H.D.N.S., email
E/19/170, JAYAWARDHANA, email
E/19/193, KAUSHALYA N.V.K., email
E/19/205, KUMARA I.P.S.N.U., email
E/19/210, KUMARASIRI R.P.J.R., email
E/19/226, MADHUSHANKA K.G.M., email
E/19/227, MADHUSHANKA M.P.J., email
E/19/236, MANIKDIWELA W.L., email

Overview
Features
Architecture
Technologies Used
Links

Overview

The AQMS is a cutting-edge solution designed to address the increasing need for effective air quality monitoring in urban and industrial environments. Inspired by the latest advancements in Industry 4.0, our system integrates:

Real-Time Monitoring: Collects live air quality data from IoT sensors.
Predictive Analytics: Uses machine learning to forecast trends and identify anomalies.
Interactive Visualization: Offers 3D views of air quality metrics for intuitive understanding.

This platform is open-source, scalable, and modular, making it adaptable to various use cases, including urban planning, industrial compliance, and public health monitoring.

description of the real world problem and solution, impact

Features

🌟 Core Features:

IoT-Driven Monitoring: Seamless integration with sensors using MQTT and AMQP protocols.
AI-Powered Predictions: Accurate forecasting of air quality metrics like AQI, PM2.5, and PM10.
Data Storage & Retrieval: Efficient time-series storage with InfluxDB.
Interactive Dashboards: User-friendly interfaces built with Grafana.
3D Visualization: Immersive models built with Unity, integrated into Grafana dashboards.
Scalability: Kubernetes-based deployment ensures high availability and fault tolerance.

🌐 Additional Capabilities:

Support for multi-region monitoring.
Configurable alerts and notifications.
Historical data analysis and trend visualization.
Modular architecture for easy extensibility.
Ditto Integration: Advanced capabilities for managing and simulating IoT devices.

Architecture

The AQMS is built on a microservices architecture, ensuring modularity, scalability, and maintainability. Below is a high-level overview of the system:

Components:

1. IoT Layer

Sensors deployed in the field collect air quality data.
Data is transmitted using MQTT and AMQP protocols to the system.
Eclipse Ditto: Manages the digital twin representation of IoT devices, enabling seamless integration and monitoring.

2. Processing Layer:

Apache Kafka: Handles real-time data streaming.
Kafka-ML: Integrates machine learning models for predictive analytics.

3. Storage Layer

InfluxDB: Stores time-series data from IoT sensors.

4. Visualization Layer:

Grafana: Displays real-time and historical data in customizable dashboards.
Unity: Provides interactive 3D models for visual representation.

5. Deployment Layer:

Docker: Packages services into containers.
Kubernetes: Orchestrates and manages containerized services.

🔧 Technologies Used

Backend Technologies

Eclipse Ditto: For digital twin creation and management.
Apache Kafka: For real-time data streaming and processing.
Node.js: For building high-performance APIs.

Data Management

InfluxDB: Time-series database for storing sensor data.
Telegraf: Data collection agent for integrating with InfluxDB.

Visualization

Grafana: For creating custom dashboards and panels.
Unity: For 3D visualization of air quality metrics.

Machine Learning

TensorFlow & PyTorch: For building and deploying predictive models.
Kafka-ML: For seamless integration of ML models with data streams.

Deployment

Docker: For containerizing microservices.
Kubernetes: For orchestrating containerized services. …..