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  Published Paper Details:

  Paper Title

IoT Driven Machine Learning Platform for Real Time Environmental Monitoring

  Authors

  K Durga Charan,  M Mounika,  Ravindra Changala,  Annapurna Gummadi

  Keywords

: IoT, environmental monitoring, air pollution, water pollution, sensors, machine Learning.

  Abstract


The environment faces real problems due to a number of variables, including radiation, water, and air pollution. Sufficient oversight is required to ensure that a healthy society and sustainable global growth are maintained. Thanks to developments in the internet of things (IoT) and the creation of contemporary sensors, environment monitoring has evolved into a smart environment monitoring (SEM) system in recent years. People are becoming more aware of their surroundings in the last several years. It is because of this awareness that a trustworthy environmental monitoring system has to be created. There are industrial uses for environmental air quality monitoring systems as well. The air in heavy industries like mining may get contaminated by several dangerous gases. An environmental monitoring system may be able to save workers' lives in such dangerous circumstances. There are problems with data collecting, data administration, connections, and power consumption in such large-scale sensor deployments. IoT technology is ideal for this kind of requirement. This article proposes an Internet of Things (IoT) based architecture that uses sensors, microcontrollers, and IoT based technologies to efficiently monitor changes in an environment. The suggested module allows users to keep an eye on temperature, humidity, and the presence of hazardous gases in both indoor and outdoor environments. Through an internet connection, the user may access the data stored on the web server from anywhere in the globe. The suggested work develops a web application to give the user essential information. Additionally, a notification for significant changes in the sensor data may be set up by the user. In contrast to other systems that are closely comparable, the suggested method is accurate, affordable, and easy to use. In addition, it features modules for simple data visualisation and monitoring and is cloud-based. The system has undergone many phases of evaluation. Following extensive testing under various circumstances, it demonstrates a high level of accuracy and dependability.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2310629

  Paper ID - 245738

  Page Number(s) - f529-f538

  Pubished in - Volume 11 | Issue 10 | October 2023

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  K Durga Charan,  M Mounika,  Ravindra Changala,  Annapurna Gummadi,   "IoT Driven Machine Learning Platform for Real Time Environmental Monitoring", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.11, Issue 10, pp.f529-f538, October 2023, Available at :http://www.ijcrt.org/papers/IJCRT2310629.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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