Advances in Smart Environment Monitoring Systems Using IoT and Sensors
Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a...
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doaj-2123e5fa902a48348f20982c7ded5ca32020-11-25T03:14:18ZengMDPI AGSensors1424-82202020-05-01203113311310.3390/s20113113Advances in Smart Environment Monitoring Systems Using IoT and SensorsSilvia Liberata Ullo0Ganesh Ram Sinha1Engineering Department, Università degli Studi del Sannio, 82100 Benevento, ItalyMyanmar Institute of Information Technology (MIIT), 05053 Mandalay, MyanmarAir quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested.https://www.mdpi.com/1424-8220/20/11/3113environmentpollutioninternet of things (IoT)sensorssmart environment monitoring (SEM)smart sensor |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Silvia Liberata Ullo Ganesh Ram Sinha |
spellingShingle |
Silvia Liberata Ullo Ganesh Ram Sinha Advances in Smart Environment Monitoring Systems Using IoT and Sensors Sensors environment pollution internet of things (IoT) sensors smart environment monitoring (SEM) smart sensor |
author_facet |
Silvia Liberata Ullo Ganesh Ram Sinha |
author_sort |
Silvia Liberata Ullo |
title |
Advances in Smart Environment Monitoring Systems Using IoT and Sensors |
title_short |
Advances in Smart Environment Monitoring Systems Using IoT and Sensors |
title_full |
Advances in Smart Environment Monitoring Systems Using IoT and Sensors |
title_fullStr |
Advances in Smart Environment Monitoring Systems Using IoT and Sensors |
title_full_unstemmed |
Advances in Smart Environment Monitoring Systems Using IoT and Sensors |
title_sort |
advances in smart environment monitoring systems using iot and sensors |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-05-01 |
description |
Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested. |
topic |
environment pollution internet of things (IoT) sensors smart environment monitoring (SEM) smart sensor |
url |
https://www.mdpi.com/1424-8220/20/11/3113 |
work_keys_str_mv |
AT silvialiberataullo advancesinsmartenvironmentmonitoringsystemsusingiotandsensors AT ganeshramsinha advancesinsmartenvironmentmonitoringsystemsusingiotandsensors |
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