This project presents an IoT - empowered front-end mobile and desktop application integrated with a conventional machine learning model to detect and predict defined air quality parameters in order to help individuals, communities and necessary authorities to take action against the deterioration of the air quality. The project was executed as an academic fulfillment at the Department of Electronic and Telecommunication Engineering, University of Moratuwa, Sri Lanka.

1. Airspec
F o r b e t t e r a i r q u a l i t y
An IoT-empowered air quality monitoring system integrated with a machine
learning framework to detect and predict defined air quality parameters
Group 13
EN2560 - Internet of Things Design and Competition
Department of Electronic and Telecommunication Engineering

2. AirSPEC
AirSPEC is an IoT - empowered
front-end
mobile and desktop application
integrated with a
conventional machine learning
model to detect and predict
defined air quality parameters
in order to
help individuals, communities and
necessary authorities
to TAKE ACTION
against the deterioration of
the air quality.
Airspec
F o r b e t t e r a i r q u a l i t y

3. The balanced air quality is of utmost
importance to the environmental
existence
Deterioration of air quality has
deleterious impacts on health,
environment and economy
The statistics of the air quality in global
and local contexts in last hundred
years emphasize the need for better air
quality monitoring approaches
Air Quality Deteriorization

4. Source - https://web.northeastern.edu/r/
Source - economynext.com

5. 6% 10%
World wide deaths in 2017 Worst condition cities
Drawbacks of
Current Settings
Some air quality detections are inaccurate
Some recommended cities were not safe
Took unnecessary actions for unwanted areas.

6. The Objective
An IoT-empowered real-time approach based on a machine
learning framework to efficiently
detect and predict defined air quality parameters
using an public API,
with the primary motivation of helping individuals, communities
and necessary authorities to take action at their levels

7. Our Approach
Machine learning
integrated node-
red framework with
the dashboard
Node-MCU based
web server
utilizing the
publish-subscribe
architecture
User-ergonomic,
integrated front-
end web and
mobile application

8. Airspec
F o r b e t t e r a i r q u a l i t y
Node-red
Framework
Initial data processing and
visualization
Location and temporal variations
Visualization of past and forecasted
data
Decision-tree prediction for air
quality parameters
A conventional machine learning
framework
File Management System
A user-ergonomic file and data
management and visualization system

9. Airspec
F o r b e t t e r a i r q u a l i t y
Node-red
Dashboard

10. Airspec
F o r b e t t e r a i r q u a l i t y
Node-MCU as the
web server
Node-MCU as the web server
Facilitate data communication between
the node-red framework and the mobile
client
Publisher-Subscriber architecture
through MQTT protocol
Via mosquitto broker
Arduino-based implementation
ESP8266WiFi.h, PubSubClient.h and
ESP8266WebServer.h

11. Airspec
F o r b e t t e r a i r q u a l i t y
Mobile client via
web portal
A web portal has been created for users to
input location coordinates from one web page
and obtains air quality data from another web
page
HTML has been used to design the content to
be displayed on the web page
CSS has been used for describing the
presentation of the document written HTML
.

12. Virtual Tour of
AirSPEC web
application

13. The Integrated
front-end Mobile
Application with a
user-friendly
interface
Key Features
Access to current
location
Visual representation
of data
User-friendly with a
simple interface
Ability to obtain
location coordinates

14. This system is an IoT-empowered
real-time approach based on a
machine learning framework to
efficiently
detect and predict defined air
quality parameters using a user
friendly web portal and a mobile
application
We believe that this system has
more room for improvements and
this will facilitate a wide array of
applications, especially in health
and environmental domains
Airspec
F o r b e t t e r a i r q u a l i t y
Conclusion

15. Nuwan Bandara
Sahan Hettiarachchi
Prabhani Athukorala
Project
Team

16. Thank you