pollution monitoring using IOT

1. Challenge #2: Pollution Monitoring
Group Name: RAS_Pi
Group leader: Amartya Thorat
Group Members:
1. Amartya Thorat
2. Rushikesh Kasture
3. Siddharth Malang
4. Prathmesh Wagh

2. OVERVIEW
 To measure and predict air pollution in a more
cost-effective and efficient way using IOT by
building the network via mobile and static
location sensors to detect origin of the air
pollutant and resource.
(static location sensors: devices placed on
street lamps, network towers, buildings
gives air pollution readings)
(mobile location sensors: devices placed
on public transport, quadcopter gives
readings of different areas.)

3. Description of
Idea /Solution
 To find out the origin of the air pollution
we have to form the network through the
smart city using the public transport,
street lamps, network towers, buildings
and quadcopter.
 The data sent using different gas
sensors attached to network sources.
 The data collected from the different
sources are using microcontroller and
sensors is deployed on cloud platform
via secured gateway.
 Analysis and data visualization is done
with the help of different sorting
techniques and represented graphically
for user interface.
 Also aware the citizens about the air
polluted areas to overcome and control
the air pollution.

4. CONNECTIVITY & DEVICE
MANAGEMENT
Connectivity services
utilizing Wi-Fi/ethernet.
Secure connection
Data analysis:
Providing analytics &
machine learning as
service, across multiple
data resource
Data provider:
Providing air quality,
network, sensor dataset
User interface:
Data visualization
Graphical Representation

6. Representation:
 Graphical Representation as
per the area scaled according
to the air pollution.

7. Requirements
 Raspberry Pi 3B, Gas Sensors(MQ135,MQ7,MQ4)
 Arduino UNO, Registers, Power Supply
 Matplotlib library
 Python 3.7 & lib.
 Flask(Web Development)
 Adobe Photoshop, Illustrator, Coral Draw for designing
of Application User Interface.
 Machine Learning for Pollution Recommendation System
for each location.