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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10536912 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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