Data reinforcement control technique‑based monitoring and controlling of environmental factors for IoT applications
In recent years, environmental monitoring is essential to minimize environmental problems. However, the need for more and more aquatic ecosystems from pollution, increasing climate change, and direct environment loss, so that they can continue to be thoroughly monitored for risks. In this system...
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my.uthm.eprints.74942022-08-15T03:01:00Z http://eprints.uthm.edu.my/7494/ Data reinforcement control technique‑based monitoring and controlling of environmental factors for IoT applications Mehbodniya, Abolfazl Haq, Mohd Anul Kumar, Anil Ismail, Mohd Erfy Dahiya, Priyanka Karupusamy, Sathishkumar T Technology (General) In recent years, environmental monitoring is essential to minimize environmental problems. However, the need for more and more aquatic ecosystems from pollution, increasing climate change, and direct environment loss, so that they can continue to be thoroughly monitored for risks. In this system, the implementation of a data reinforcement control technique (DRCT) using the Internet of Things (IoT) systems is presented to aquatic inquiry environments. In particular, it describes an automated system using data from the design and implementation of recording environ�ments that monitor sensors and installation parameters in lakes and rivers. Design and low-cost real-time monitoring, alert worker development, polluted notification data are confirmed around. Various parameters such as air quality, temperature, humidity, and PIC controller sound intensity sensor, collection, and upload data to the cloud using the Wi-Fi module. It then forwards to a digital dashboard for confirmation on the web via a smartphone or cloud platform exception notification to alert the user. The industry will be the power of closed industrial electricity if they do not take any steps to reduce pollution within a specified period until they are paid under government regulations and pollution control and the amount of pollution in the city. The proposed system is simulated in the PROTEUS 8.1 simulator to analyze the system output. Springer 2022 Article PeerReviewed text en http://eprints.uthm.edu.my/7494/1/J14383_4830cb99648f93a318f1cd651bf1f02b.pdf Mehbodniya, Abolfazl and Haq, Mohd Anul and Kumar, Anil and Ismail, Mohd Erfy and Dahiya, Priyanka and Karupusamy, Sathishkumar (2022) Data reinforcement control technique‑based monitoring and controlling of environmental factors for IoT applications. Arabian Journal of Geosciences, 15. pp. 1-8. https://doi.org/10.1007/s12517-022-09917-3 |
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T Technology (General) Mehbodniya, Abolfazl Haq, Mohd Anul Kumar, Anil Ismail, Mohd Erfy Dahiya, Priyanka Karupusamy, Sathishkumar Data reinforcement control technique‑based monitoring and controlling of environmental factors for IoT applications |
description |
In recent years, environmental monitoring is essential to minimize environmental problems. However, the need for
more and more aquatic ecosystems from pollution, increasing climate change, and direct environment loss, so that
they can continue to be thoroughly monitored for risks. In this system, the implementation of a data reinforcement
control technique (DRCT) using the Internet of Things (IoT) systems is presented to aquatic inquiry environments.
In particular, it describes an automated system using data from the design and implementation of recording environ�ments that monitor sensors and installation parameters in lakes and rivers. Design and low-cost real-time monitoring,
alert worker development, polluted notification data are confirmed around. Various parameters such as air quality,
temperature, humidity, and PIC controller sound intensity sensor, collection, and upload data to the cloud using the
Wi-Fi module. It then forwards to a digital dashboard for confirmation on the web via a smartphone or cloud platform
exception notification to alert the user. The industry will be the power of closed industrial electricity if they do not take
any steps to reduce pollution within a specified period until they are paid under government regulations and pollution
control and the amount of pollution in the city. The proposed system is simulated in the PROTEUS 8.1 simulator to
analyze the system output. |
format |
Article |
author |
Mehbodniya, Abolfazl Haq, Mohd Anul Kumar, Anil Ismail, Mohd Erfy Dahiya, Priyanka Karupusamy, Sathishkumar |
author_facet |
Mehbodniya, Abolfazl Haq, Mohd Anul Kumar, Anil Ismail, Mohd Erfy Dahiya, Priyanka Karupusamy, Sathishkumar |
author_sort |
Mehbodniya, Abolfazl |
title |
Data reinforcement control technique‑based monitoring
and controlling of environmental factors for IoT applications |
title_short |
Data reinforcement control technique‑based monitoring
and controlling of environmental factors for IoT applications |
title_full |
Data reinforcement control technique‑based monitoring
and controlling of environmental factors for IoT applications |
title_fullStr |
Data reinforcement control technique‑based monitoring
and controlling of environmental factors for IoT applications |
title_full_unstemmed |
Data reinforcement control technique‑based monitoring
and controlling of environmental factors for IoT applications |
title_sort |
data reinforcement control technique‑based monitoring
and controlling of environmental factors for iot applications |
publisher |
Springer |
publishDate |
2022 |
url |
http://eprints.uthm.edu.my/7494/1/J14383_4830cb99648f93a318f1cd651bf1f02b.pdf http://eprints.uthm.edu.my/7494/ https://doi.org/10.1007/s12517-022-09917-3 |
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