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ArtEMon: Artificial Intelligence and Internet of Things Powered Greenhouse Gas Sensing for Real-Time Emissions Monitoring

Greenhouse gas (GHG) emissions reporting and sustainability are increasingly important for businesses around the world. Yet the lack of a single standardised method of measurement, when coupled with an inability to understand the true state of emissions in complex logistics activities, presents enor...

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Autores principales: Yavari, Ali, Mirza, Irfan Baig, Bagha, Hamid, Korala, Harindu, Dia, Hussein, Scifleet, Paul, Sargent, Jason, Tjung, Caroline, Shafiei, Mahnaz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10536912/
https://www.ncbi.nlm.nih.gov/pubmed/37766027
http://dx.doi.org/10.3390/s23187971
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author Yavari, Ali
Mirza, Irfan Baig
Bagha, Hamid
Korala, Harindu
Dia, Hussein
Scifleet, Paul
Sargent, Jason
Tjung, Caroline
Shafiei, Mahnaz
author_facet Yavari, Ali
Mirza, Irfan Baig
Bagha, Hamid
Korala, Harindu
Dia, Hussein
Scifleet, Paul
Sargent, Jason
Tjung, Caroline
Shafiei, Mahnaz
author_sort Yavari, Ali
collection PubMed
description Greenhouse gas (GHG) emissions reporting and sustainability are increasingly important for businesses around the world. Yet the lack of a single standardised method of measurement, when coupled with an inability to understand the true state of emissions in complex logistics activities, presents enormous barriers for businesses to understanding the extent of their emissions footprint. One of the traditional approaches to accurately capturing and monitoring gas emissions in logistics is through using gas sensors. However, connecting, maintaining, and operating gas sensors on moving vehicles in different road and weather conditions is a large and costly challenge. This paper presents the development and evaluation of a reliable and accurate sensing technique for GHG emissions collection (or monitoring) in real-time, employing the Internet of Things (IoT) and Artificial Intelligence (AI) to eliminate or reduce the usage of gas sensors, using reliable and cost-effective solutions.
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spelling pubmed-105369122023-09-29 ArtEMon: Artificial Intelligence and Internet of Things Powered Greenhouse Gas Sensing for Real-Time Emissions Monitoring Yavari, Ali Mirza, Irfan Baig Bagha, Hamid Korala, Harindu Dia, Hussein Scifleet, Paul Sargent, Jason Tjung, Caroline Shafiei, Mahnaz Sensors (Basel) Article Greenhouse gas (GHG) emissions reporting and sustainability are increasingly important for businesses around the world. Yet the lack of a single standardised method of measurement, when coupled with an inability to understand the true state of emissions in complex logistics activities, presents enormous barriers for businesses to understanding the extent of their emissions footprint. One of the traditional approaches to accurately capturing and monitoring gas emissions in logistics is through using gas sensors. However, connecting, maintaining, and operating gas sensors on moving vehicles in different road and weather conditions is a large and costly challenge. This paper presents the development and evaluation of a reliable and accurate sensing technique for GHG emissions collection (or monitoring) in real-time, employing the Internet of Things (IoT) and Artificial Intelligence (AI) to eliminate or reduce the usage of gas sensors, using reliable and cost-effective solutions. MDPI 2023-09-19 /pmc/articles/PMC10536912/ /pubmed/37766027 http://dx.doi.org/10.3390/s23187971 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yavari, Ali
Mirza, Irfan Baig
Bagha, Hamid
Korala, Harindu
Dia, Hussein
Scifleet, Paul
Sargent, Jason
Tjung, Caroline
Shafiei, Mahnaz
ArtEMon: Artificial Intelligence and Internet of Things Powered Greenhouse Gas Sensing for Real-Time Emissions Monitoring
title ArtEMon: Artificial Intelligence and Internet of Things Powered Greenhouse Gas Sensing for Real-Time Emissions Monitoring
title_full ArtEMon: Artificial Intelligence and Internet of Things Powered Greenhouse Gas Sensing for Real-Time Emissions Monitoring
title_fullStr ArtEMon: Artificial Intelligence and Internet of Things Powered Greenhouse Gas Sensing for Real-Time Emissions Monitoring
title_full_unstemmed ArtEMon: Artificial Intelligence and Internet of Things Powered Greenhouse Gas Sensing for Real-Time Emissions Monitoring
title_short ArtEMon: Artificial Intelligence and Internet of Things Powered Greenhouse Gas Sensing for Real-Time Emissions Monitoring
title_sort artemon: artificial intelligence and internet of things powered greenhouse gas sensing for real-time emissions monitoring
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10536912/
https://www.ncbi.nlm.nih.gov/pubmed/37766027
http://dx.doi.org/10.3390/s23187971
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