• Ozone (O
3
),
• Ammonia (NH
3
), and
• Nitrogen dioxide (NO
2
).
They equipped the drones with compact sensors to provide data at nearly any time at a
three-dimensional axis. This important feature can help in reducing of sensors installed in the
ground-based monitoring system used in other smart city projects. Thus, reducing the total cost
of monitoring air quality. Additionally, the system allows the user to collect data for production
monitoring, issue discovery, and local climate control.
The project needed a wireless sensor network with the listed requirements: portability, low
cost, must be capable of storing data, and its wireless transmission. They also needed time and
location stamping of the gas measurements. This was done through an integrated GPS module
in the system. They must have the ability to integrate multiple protocols into a single node. This
gave them the capability to implement multi-WSN architectures under which self-governing
communications can take place within a single network.
The drone had one sensor board and a second one to hold the battery. The sensors had to
do the job of taking the measurements while the drone gets airborne. Then, the measurements
get sent via GPRS, the received information will directly get processed to the database.
This kind of flexible platforms to monitor the air quality can greatly resolve our future
problems. It is a truly innovative technique to visualize the air pollutants in both 2D and 3D.
By using such drones, we will be able to create a 3D mesh of the study area. This makes the
data geo-referenced 3D model and helpful to understand the distribution of the air pollutants.
Presently, the CO2 emissions are the biggest hazard to our cities. By knowing the distribution of
CO2 in an area, we can plan to grow plants to decrease CO
2
. Air pollution is the biggest concern
in industrial areas, harbors and airports. We can use multiple 3D air quality modelling drones
in such areas to better analyze the area and plan eective measures to decrease air pollution.
Main contributions required for the project:
• Their aim was to implement a unique system that is UAV vision guided aerial-ground AQI
system. They wanted to deploy it and evaluate it in the real-world testbed and scenarios.
• The proposed system should use a vision-based sensing system that can understand the
correlation among the raw haze images and corresponding AQI scale distribution.
• The proposed entropy-based interface model for WSN can acquire a true high accuracy
in real time. It can also do future AQI prediction.
• The wake-up mechanism will be connected to the aerial vision technique and on-
ground WSN. This will save greatly save energy consumptions of the on-ground sensor
network without sacrificing high interference and prediction accuracy.
Advantages of using 3D air quality modelling drones:
• These drones can monitor air quality in real time at various locations together.
• All the drones can be connected to a single network making the system a unified system.
• It is a more practical and effective solution than ground-based systems.
• The drones can further be equipped by surveillance capabilities.
After building the system, emissions of CO
2
from the chimney of the university and the
parking lot were evaluated. It is not the first time where airborne drones were used in a similar
system. In Russia, a smart monitoring system was developed by using drones to measure
pollution in a water reservoir in Eurasia.
Chapter 1 AnAnswer to the NextGeneration Automated World 21
Internet_of_Things_CH01_pp001-026.indd 21 9/3/2019 10:12:18 AM
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