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Detection of Vehicle Emissions through Green
IoT for Pollution Control
Department of Computer Science and Engineering, Amrita
School of Engineering, Coimbatore
Presentation by : Mr. Jigar Makhija, Mr. Nakkeeran M
Guide : Dr. V. Anantha Narayanan
(Paper ID – 125)
1Amrita School of Engineering, Coimbatore25 February 2020
OUTLINE
1. Introduction
2. Objective
3. Literature Survey
4. Novelty
5. Methodology
6. Block Diagram / Flowchart
7. Experimental Setup
8. Result and Conclusion
9. Future Scope
10. References
25 February 2020 Amrita School of Engineering, Coimbatore 2
1. INTRODUCTION
25 February 2020 Amrita School of Engineering, Coimbatore 3
>> 1. Introduction
>> 1. Introduction
 In recent years, the usage of vehicles causes more than 60% of
carbon monoxide in the atmosphere is contaminated.
 People are not strictly adhere to the rules and regulations of
Emission standards, Pollution under Control (PUC) certification
from Pollution Control Board.
 Smart cities use Internet of Things (IoT) devices such as
connected sensors, lights, and meters to collect and analyze
data.
 Green-IoT has embraced energy saving, sustainability and
efficiency to minimize the greenhouse gas impact of IoT
devices, offering optimum energy-efficient solutions to
decrease the effect of greenhouse generated by the IoT
applications themselves with low power consumption and low
cost.
25 February 2020 Amrita School of Engineering, Coimbatore 4
2. OBJECTIVE
25 February 2020 Amrita School of Engineering, Coimbatore 5
>> 2. Objective
>> 2. Objective
The goal of this paper is to prevent pollution
by Green IoT by integrating the embedded
wireless detection and notification system
through the Green-IoT model in order to
reduce vehicle emissions.
The aim of Green IoT has emerged for
environmentally friendly and energy
efficiency to reduce the green-house effect
by creating a greener society.
25 February 2020 Amrita School of Engineering, Coimbatore 6
3. LITERATURE SURVEY
25 February 2020 Amrita School of Engineering, Coimbatore 7
>> 3. Literature Survey
>> 3. Literature Survey
S.
No.
Journal/Conference
Paper
Work Carried out Inference
1.
Real-time air pollution
exposure and vehicle
emissions estimation using
IoT, GNSS measurements
and web-based simulation
models [1] 2018, August
Indicating the driver in real-
time of their air quality
exposure and emissions.
IoT device, a smartphone application
and web-based simulation model
were created but based on the
geological factors and remote
locations where there is no facility of
network this application may fail.
2.
IOT Based Vehicle Anti-
Collision And Pollution
Control System [10] 2019,
June
This system measures the
ppm level of emitted gases
and display it as well as
alerts the user when
vehicle is not moving and
pollution level exceeds the
threshold value
Centralized communication system
was missing in this simulation project
and the system is working as single
stand alone for each vehicle to alert
the user.
3.
IoT Based Automobile Air
Pollution Monitoring
System [11] 2018, January
detecting the vehicles
which emit the pollution
greater than the standard
limit
Since lot of sensors are applied on the
development the cost will increase
and there is not such single sensor
which can give the data all together.
25 February 2020 Amrita School of Engineering, Coimbatore 8
>> 3. Literature Survey
S.
No.
Journal/Conference
Paper
Work Carried out Inference
4.
Development of IoT
based Vehicular
Pollution Monitoring
System [8] 2015,
October
Reports the status of air quality
whenever needed to the
environmental agencies. The
measured data is also shared to
vehicle owner, traffic department
and agencies of national
environment.
Using RFID the necessary
emission pollutants are not
collected for monitoring and
local storage is done, it is real
time solution but major prone
to network issues and not
appropriate to outside the city.
5.
Air Quality Monitoring
System Based on IoT
using Raspberry Pi [9]
2017, May
A real-time standalone air quality
monitoring system, Internet of
Things converging with cloud
computing offers a novel technique
for better management of data
coming from different sensors.
WiFi Adapter is used as the
connecting point for data
transfer. Basically there is no
centralized point of contact
created from different sensor
nodes for collect the
information.
6.
Development of an
IoT based Air Quality
Monitoring System
[16]
installing multiple sensor stations
in various locations, sensor stations
communicate with cloud over HTTP
protocol, and through a web
application we can see the data
Multiple sensors connected and
implementation in various
location using WIFI requires
strong internet connectivity and
again it increase the cost and
critical in remote locations.
25 February 2020 Amrita School of Engineering, Coimbatore 9
4. NOVELTY
25 February 2020 Amrita School of Engineering, Coimbatore 10
>> 4. Novelty
>> 4. Novelty
An approach towards Non-Cellular Wireless Communication LoRa(Long Range)
25 February 2020 Amrita School of Engineering, Coimbatore 11
Technology Range Max Output Security Limitations
Wifi 50m (indoors) 0.1 W
WiFi security has
suffered from a lot of
issues recently.
It has a limited range and it
requires a large amount of
power, so it is not suitable for
battery-operated devices.
GSM
35km(technical
limit)
2 W
This old norm is also
targeted by exploits
It requires a lot of energy, so
it's not suitable for devices
running on batteries.
LoRaWAN
5-10km typical
(line of sight)
0.025 W
Unique set of AES keys
are used.
It is not a good choice for IoT
deviceswhich needs an
immediate feedback loop.
Fig. 1. Cellular vs. Non-Cellular
Table 1. Comparison based on technologies
LoRaWAN® distance world record broken.
766 km (476 miles) using 25mW transmission
power.
Source:https://www.thethingsnetwork.org/articl
e/lorawan-distance-world-record
5. METHODOLOGY
25 February 2020 Amrita School of Engineering, Coimbatore 12
>> 5. Methodology
>> 5. Methodology
The Proposed System comprised of three layers:
1) Vehicle On-board : TGS2201 Gas Sensor,
DRF1276Dm LoRa Module,
Node MCU ESP8266
2) Road Side Units : DRF1276Dm LoRa Module + Raspberry Pi-3
3) Cloud IoT Platform : AWS IoT Core + Amazon RDS
MySQL Work-Bench
25 February 2020 Amrita School of Engineering, Coimbatore 13
6. BLOCK DIAGRAM / FLOWCHART
25 February 2020 Amrita School of Engineering, Coimbatore 14
>> 6. Block Diagram / Flowchart
>> 6. Block Diagram
25 February 2020 Amrita School of Engineering, Coimbatore 15
Fig. 2. Block diagram of proposed IoT based Vehicular AQ Monitoring System.
>> 6. Flowchart
25 February 2020 Amrita School of Engineering, Coimbatore 16
Fig. 3. Flowchart of proposed IoT based VehicularAQ Monitoring System.
7. EXPERIMENTAL SETUP
25 February 2020 Amrita School of Engineering, Coimbatore 17
>> 7. Experimental Setup
>> 7. Experimental Setup
Components used:
 TGS 2201 Sensor for gasoline and diesel exhaust gas
 NodeMCU
 LoRa Module
 Raspberry Pi 3
25 February 2020 Amrita School of Engineering, Coimbatore 18
Fig. 4. LoRa connected to NodeMCU along
with Sensors
Fig. 5. LoRa connected to Rasberry Pi
8. RESULTS AND CONCLUSION
25 February 2020 Amrita School of Engineering, Coimbatore 19
>> 8. Result and Conclusion
>> 8. Result and Conclusion
WMTC emissions limits for gasoline vehicles
Two wheeler petrol vehicle emission standards are specified in the following
table[12].
Table2. Emissions Limits
25 February 2020 Amrita School of Engineering, Coimbatore 20
Class Pollutants Limits(g/km)
Class 1 & Sub Class
2.1
CO 1.87
HC+NOX 1.08
Sub-Class 2.2
CO 2.62
HC+NOX 0.92
Sub-Class 3.1
CO 2.62
HC+NOX 0.55
Sub-Class 3.2
CO 2.62
HC+NOX 0.55
>> 8. Result and Conclusion
25 February 2020 Amrita School of Engineering, Coimbatore 21
Fig. 6.Shows the Pollution data that has been collected from different vehicles into
MySQL Workbench.
Fig. 7.AWS IoT Cloud-Creating Thing
>> 8. Conclusion
 In the proposed work it gives a solution by creating a smart embedded
device along with alert notifications for pollution prevention from vehicles
through green IoT .
 This system tries to achieve a real-time pollution monitoring based on smart
IoT mechanism.
 Results are obtained with promising facts, which is a significant contribution
to the IoT-based smart monitoring systems.
 To improve device configuration and reliabiity, the device needs more
experimental validation.
 Furthermore, upgrades to hardware issues were also designed to adapt the
device to specific cases.
25 February 2020 Amrita School of Engineering, Coimbatore 22
9. FUTURE SCOPE
25 February 2020 Amrita School of Engineering, Coimbatore 23
>> 9. Future Scope
>> 9. Future Scope
The future works can be extended to
develop a safe information sharing platform
with edge computing to support the
pollution predective IoT-based system's
using predictive analytics.
This can be very useful in helping the
automotive industry to embedded the
devices and by monitoring AQ in on-road so
that we could detect vehicle maintenance in
real time and healthify the society.
25 February 2020 Amrita School of Engineering, Coimbatore 24
10. REFERENCES
25 February 2020 Amrita School of Engineering, Coimbatore 25
>> 10. References
>> 10. References
1. Thibault, L., Pognant-Gros, P., Degeilh, P., Thanabalasingam, K., Sabiron, G., &Voise,
L. Real-Time Air Pollution Exposure and Vehicle Emissions Estimation Using IoT,
GNSS Measurements and Web-Based Simulation Models. In 2018 IEEE 88th
Vehicular Technology Conference (VTC-Fall)(2018, August) (pp. 1-5). IEEE
2. Sharma, S. B., Jain, S., Khirwadkar, P., & Kulkarni, S. The effects of air pollution on
the environment and human health. Indian Journal of Research in Pharmacy and
Biotechnology, 1(3), (2013)391-396.
3. Gupta, K., & Hall, R. P. The Indian perspective of smart cities. In 2017 Smart City
Symposium Prague (SCSP)(2017, May) (pp. 1-6). IEEE.
4. Mehmood, Y., Ahmad, F., Yaqoob, I., Adnane, A., Imran, M., & Guizani, S. Internet-
of-things-based smart cities: Recent advances and challenges. IEEE Communications
Magazine, 55(9),(2017) 16-24.
5. Shaikh, F. K., Zeadally, S., &Exposito, E. Enabling technologies for green internet of
things. IEEE Systems Journal, 11(2),(2015) 983-994.
6. Haldorai, A., Ramu, A., &Murugan, S. Smart Sensor Networking and Green
Technologies in Urban Areas. In Computing and Communication Systems in Urban
Development (2019) (pp. 205-224). Springer, Cham.
7. Center for Industrial Sensors and Measurements (CISM), Department of Materials
Science and Engineering, The Ohio State University, Columbus, USA
8. Rushikesh, R., & Sivappagari, C. M. R. Development of IoT based vehicular pollution
monitoring system. In 2015 International Conference on Green Computing and
Internet of Things (ICGCIoT)(2015, October)(pp. 779-783). IEEE.
25 February 2020 Amrita School of Engineering, Coimbatore 26
>> 10. References
25 February 2020 Amrita School of Engineering, Coimbatore 27
9. Kumar, S., & Jasuja, A. Air quality monitoring system based on IoT using
Raspberry Pi. In 2017 International Conference on Computing, Communication
and Automation (ICCCA)(2017, May)(pp. 1341-1346). IEEE.
10. Amin, A. B., Patel, H. P., Vaghela, S. P., & Patel, R. R. IOT Based Vehicle Anti-
Collision And Pollution Control System. In 2019 3rd International conference on
Electronics, Communication and Aerospace Technology (ICECA) (2019, June)
(pp. 108-110). IEEE.
11. Gupta, K. and Rakesh, N, IoT Based Automobile Air Pollution Monitoring
System. In 2018 8th International Conference on Cloud Computing, Data
Science & Engineering (Confluence) 2018, January (pp. 14-15). IEEE.
12. Emission Standards- https://www.araiindia.com/pdf/Indian Emission
Regulation Booklet.pdf
13. LoRa - https://www.instructables.com/id/Communication-LoRa-ESP8266-
Radio-RFM95/
14. Raspberry Pi - https://www.quickanddirtytips.com/tech/computers/what-is-
the-raspberry-pi
15. Raspberry Pi Code-https://iotdesignpro.com/projects/how-to-send-data-to-
thingspeak-cloud-using-raspberry-pi
>> 10. References
25 February 2020 Amrita School of Engineering, Coimbatore 28
16. Sreevas, R., Shanmughasundaram, R., VRL, Swami, Vadali., “Development of an
IoT based Air Quality Monitoring System”, International Journal of Innovative
Technology and Exploring Engineering, vol. 8, issue 10S, pp. 23-28, 2019
17. Spandana, G., Shanmughasundram,R., "Design and Development of Air
Pollution Monitoring System for Smart Cities," 2018 Second International
Conference on Intelligent Computing and Control Systems (ICICCS), Madurai,
India, 2018, pp. 1640-1643.
THANK YOU…
25 February 2020 Amrita School of Engineering, Coimbatore 29

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125 green iot

  • 1. Detection of Vehicle Emissions through Green IoT for Pollution Control Department of Computer Science and Engineering, Amrita School of Engineering, Coimbatore Presentation by : Mr. Jigar Makhija, Mr. Nakkeeran M Guide : Dr. V. Anantha Narayanan (Paper ID – 125) 1Amrita School of Engineering, Coimbatore25 February 2020
  • 2. OUTLINE 1. Introduction 2. Objective 3. Literature Survey 4. Novelty 5. Methodology 6. Block Diagram / Flowchart 7. Experimental Setup 8. Result and Conclusion 9. Future Scope 10. References 25 February 2020 Amrita School of Engineering, Coimbatore 2
  • 3. 1. INTRODUCTION 25 February 2020 Amrita School of Engineering, Coimbatore 3 >> 1. Introduction
  • 4. >> 1. Introduction  In recent years, the usage of vehicles causes more than 60% of carbon monoxide in the atmosphere is contaminated.  People are not strictly adhere to the rules and regulations of Emission standards, Pollution under Control (PUC) certification from Pollution Control Board.  Smart cities use Internet of Things (IoT) devices such as connected sensors, lights, and meters to collect and analyze data.  Green-IoT has embraced energy saving, sustainability and efficiency to minimize the greenhouse gas impact of IoT devices, offering optimum energy-efficient solutions to decrease the effect of greenhouse generated by the IoT applications themselves with low power consumption and low cost. 25 February 2020 Amrita School of Engineering, Coimbatore 4
  • 5. 2. OBJECTIVE 25 February 2020 Amrita School of Engineering, Coimbatore 5 >> 2. Objective
  • 6. >> 2. Objective The goal of this paper is to prevent pollution by Green IoT by integrating the embedded wireless detection and notification system through the Green-IoT model in order to reduce vehicle emissions. The aim of Green IoT has emerged for environmentally friendly and energy efficiency to reduce the green-house effect by creating a greener society. 25 February 2020 Amrita School of Engineering, Coimbatore 6
  • 7. 3. LITERATURE SURVEY 25 February 2020 Amrita School of Engineering, Coimbatore 7 >> 3. Literature Survey
  • 8. >> 3. Literature Survey S. No. Journal/Conference Paper Work Carried out Inference 1. Real-time air pollution exposure and vehicle emissions estimation using IoT, GNSS measurements and web-based simulation models [1] 2018, August Indicating the driver in real- time of their air quality exposure and emissions. IoT device, a smartphone application and web-based simulation model were created but based on the geological factors and remote locations where there is no facility of network this application may fail. 2. IOT Based Vehicle Anti- Collision And Pollution Control System [10] 2019, June This system measures the ppm level of emitted gases and display it as well as alerts the user when vehicle is not moving and pollution level exceeds the threshold value Centralized communication system was missing in this simulation project and the system is working as single stand alone for each vehicle to alert the user. 3. IoT Based Automobile Air Pollution Monitoring System [11] 2018, January detecting the vehicles which emit the pollution greater than the standard limit Since lot of sensors are applied on the development the cost will increase and there is not such single sensor which can give the data all together. 25 February 2020 Amrita School of Engineering, Coimbatore 8
  • 9. >> 3. Literature Survey S. No. Journal/Conference Paper Work Carried out Inference 4. Development of IoT based Vehicular Pollution Monitoring System [8] 2015, October Reports the status of air quality whenever needed to the environmental agencies. The measured data is also shared to vehicle owner, traffic department and agencies of national environment. Using RFID the necessary emission pollutants are not collected for monitoring and local storage is done, it is real time solution but major prone to network issues and not appropriate to outside the city. 5. Air Quality Monitoring System Based on IoT using Raspberry Pi [9] 2017, May A real-time standalone air quality monitoring system, Internet of Things converging with cloud computing offers a novel technique for better management of data coming from different sensors. WiFi Adapter is used as the connecting point for data transfer. Basically there is no centralized point of contact created from different sensor nodes for collect the information. 6. Development of an IoT based Air Quality Monitoring System [16] installing multiple sensor stations in various locations, sensor stations communicate with cloud over HTTP protocol, and through a web application we can see the data Multiple sensors connected and implementation in various location using WIFI requires strong internet connectivity and again it increase the cost and critical in remote locations. 25 February 2020 Amrita School of Engineering, Coimbatore 9
  • 10. 4. NOVELTY 25 February 2020 Amrita School of Engineering, Coimbatore 10 >> 4. Novelty
  • 11. >> 4. Novelty An approach towards Non-Cellular Wireless Communication LoRa(Long Range) 25 February 2020 Amrita School of Engineering, Coimbatore 11 Technology Range Max Output Security Limitations Wifi 50m (indoors) 0.1 W WiFi security has suffered from a lot of issues recently. It has a limited range and it requires a large amount of power, so it is not suitable for battery-operated devices. GSM 35km(technical limit) 2 W This old norm is also targeted by exploits It requires a lot of energy, so it's not suitable for devices running on batteries. LoRaWAN 5-10km typical (line of sight) 0.025 W Unique set of AES keys are used. It is not a good choice for IoT deviceswhich needs an immediate feedback loop. Fig. 1. Cellular vs. Non-Cellular Table 1. Comparison based on technologies LoRaWAN® distance world record broken. 766 km (476 miles) using 25mW transmission power. Source:https://www.thethingsnetwork.org/articl e/lorawan-distance-world-record
  • 12. 5. METHODOLOGY 25 February 2020 Amrita School of Engineering, Coimbatore 12 >> 5. Methodology
  • 13. >> 5. Methodology The Proposed System comprised of three layers: 1) Vehicle On-board : TGS2201 Gas Sensor, DRF1276Dm LoRa Module, Node MCU ESP8266 2) Road Side Units : DRF1276Dm LoRa Module + Raspberry Pi-3 3) Cloud IoT Platform : AWS IoT Core + Amazon RDS MySQL Work-Bench 25 February 2020 Amrita School of Engineering, Coimbatore 13
  • 14. 6. BLOCK DIAGRAM / FLOWCHART 25 February 2020 Amrita School of Engineering, Coimbatore 14 >> 6. Block Diagram / Flowchart
  • 15. >> 6. Block Diagram 25 February 2020 Amrita School of Engineering, Coimbatore 15 Fig. 2. Block diagram of proposed IoT based Vehicular AQ Monitoring System.
  • 16. >> 6. Flowchart 25 February 2020 Amrita School of Engineering, Coimbatore 16 Fig. 3. Flowchart of proposed IoT based VehicularAQ Monitoring System.
  • 17. 7. EXPERIMENTAL SETUP 25 February 2020 Amrita School of Engineering, Coimbatore 17 >> 7. Experimental Setup
  • 18. >> 7. Experimental Setup Components used:  TGS 2201 Sensor for gasoline and diesel exhaust gas  NodeMCU  LoRa Module  Raspberry Pi 3 25 February 2020 Amrita School of Engineering, Coimbatore 18 Fig. 4. LoRa connected to NodeMCU along with Sensors Fig. 5. LoRa connected to Rasberry Pi
  • 19. 8. RESULTS AND CONCLUSION 25 February 2020 Amrita School of Engineering, Coimbatore 19 >> 8. Result and Conclusion
  • 20. >> 8. Result and Conclusion WMTC emissions limits for gasoline vehicles Two wheeler petrol vehicle emission standards are specified in the following table[12]. Table2. Emissions Limits 25 February 2020 Amrita School of Engineering, Coimbatore 20 Class Pollutants Limits(g/km) Class 1 & Sub Class 2.1 CO 1.87 HC+NOX 1.08 Sub-Class 2.2 CO 2.62 HC+NOX 0.92 Sub-Class 3.1 CO 2.62 HC+NOX 0.55 Sub-Class 3.2 CO 2.62 HC+NOX 0.55
  • 21. >> 8. Result and Conclusion 25 February 2020 Amrita School of Engineering, Coimbatore 21 Fig. 6.Shows the Pollution data that has been collected from different vehicles into MySQL Workbench. Fig. 7.AWS IoT Cloud-Creating Thing
  • 22. >> 8. Conclusion  In the proposed work it gives a solution by creating a smart embedded device along with alert notifications for pollution prevention from vehicles through green IoT .  This system tries to achieve a real-time pollution monitoring based on smart IoT mechanism.  Results are obtained with promising facts, which is a significant contribution to the IoT-based smart monitoring systems.  To improve device configuration and reliabiity, the device needs more experimental validation.  Furthermore, upgrades to hardware issues were also designed to adapt the device to specific cases. 25 February 2020 Amrita School of Engineering, Coimbatore 22
  • 23. 9. FUTURE SCOPE 25 February 2020 Amrita School of Engineering, Coimbatore 23 >> 9. Future Scope
  • 24. >> 9. Future Scope The future works can be extended to develop a safe information sharing platform with edge computing to support the pollution predective IoT-based system's using predictive analytics. This can be very useful in helping the automotive industry to embedded the devices and by monitoring AQ in on-road so that we could detect vehicle maintenance in real time and healthify the society. 25 February 2020 Amrita School of Engineering, Coimbatore 24
  • 25. 10. REFERENCES 25 February 2020 Amrita School of Engineering, Coimbatore 25 >> 10. References
  • 26. >> 10. References 1. Thibault, L., Pognant-Gros, P., Degeilh, P., Thanabalasingam, K., Sabiron, G., &Voise, L. Real-Time Air Pollution Exposure and Vehicle Emissions Estimation Using IoT, GNSS Measurements and Web-Based Simulation Models. In 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)(2018, August) (pp. 1-5). IEEE 2. Sharma, S. B., Jain, S., Khirwadkar, P., & Kulkarni, S. The effects of air pollution on the environment and human health. Indian Journal of Research in Pharmacy and Biotechnology, 1(3), (2013)391-396. 3. Gupta, K., & Hall, R. P. The Indian perspective of smart cities. In 2017 Smart City Symposium Prague (SCSP)(2017, May) (pp. 1-6). IEEE. 4. Mehmood, Y., Ahmad, F., Yaqoob, I., Adnane, A., Imran, M., & Guizani, S. Internet- of-things-based smart cities: Recent advances and challenges. IEEE Communications Magazine, 55(9),(2017) 16-24. 5. Shaikh, F. K., Zeadally, S., &Exposito, E. Enabling technologies for green internet of things. IEEE Systems Journal, 11(2),(2015) 983-994. 6. Haldorai, A., Ramu, A., &Murugan, S. Smart Sensor Networking and Green Technologies in Urban Areas. In Computing and Communication Systems in Urban Development (2019) (pp. 205-224). Springer, Cham. 7. Center for Industrial Sensors and Measurements (CISM), Department of Materials Science and Engineering, The Ohio State University, Columbus, USA 8. Rushikesh, R., & Sivappagari, C. M. R. Development of IoT based vehicular pollution monitoring system. In 2015 International Conference on Green Computing and Internet of Things (ICGCIoT)(2015, October)(pp. 779-783). IEEE. 25 February 2020 Amrita School of Engineering, Coimbatore 26
  • 27. >> 10. References 25 February 2020 Amrita School of Engineering, Coimbatore 27 9. Kumar, S., & Jasuja, A. Air quality monitoring system based on IoT using Raspberry Pi. In 2017 International Conference on Computing, Communication and Automation (ICCCA)(2017, May)(pp. 1341-1346). IEEE. 10. Amin, A. B., Patel, H. P., Vaghela, S. P., & Patel, R. R. IOT Based Vehicle Anti- Collision And Pollution Control System. In 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA) (2019, June) (pp. 108-110). IEEE. 11. Gupta, K. and Rakesh, N, IoT Based Automobile Air Pollution Monitoring System. In 2018 8th International Conference on Cloud Computing, Data Science & Engineering (Confluence) 2018, January (pp. 14-15). IEEE. 12. Emission Standards- https://www.araiindia.com/pdf/Indian Emission Regulation Booklet.pdf 13. LoRa - https://www.instructables.com/id/Communication-LoRa-ESP8266- Radio-RFM95/ 14. Raspberry Pi - https://www.quickanddirtytips.com/tech/computers/what-is- the-raspberry-pi 15. Raspberry Pi Code-https://iotdesignpro.com/projects/how-to-send-data-to- thingspeak-cloud-using-raspberry-pi
  • 28. >> 10. References 25 February 2020 Amrita School of Engineering, Coimbatore 28 16. Sreevas, R., Shanmughasundaram, R., VRL, Swami, Vadali., “Development of an IoT based Air Quality Monitoring System”, International Journal of Innovative Technology and Exploring Engineering, vol. 8, issue 10S, pp. 23-28, 2019 17. Spandana, G., Shanmughasundram,R., "Design and Development of Air Pollution Monitoring System for Smart Cities," 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2018, pp. 1640-1643.
  • 29. THANK YOU… 25 February 2020 Amrita School of Engineering, Coimbatore 29