ArtEMon: Artificial Intelligence and Internet of Things Powered Greenhouse Gas Sensing for Real-Time Emissions Monitoring-Reference-Cited by-同舟云学术

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

Published:2023-09-19 Issue:18 Volume:23 Page:7971
ISSN:1424-8220
Container-title:Sensors
language:en
Short-container-title:Sensors

Author:

Yavari Ali¹²^ORCID,Mirza Irfan Baig¹^ORCID,Bagha Hamid³^ORCID,Korala Harindu⁴^ORCID,Dia Hussein⁵^ORCID,Scifleet Paul⁶^ORCID,Sargent Jason⁶^ORCID,Tjung Caroline⁷,Shafiei Mahnaz¹^ORCID

Affiliation:

1. School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia

2. 6G Research and Innovation Lab, Swinburne University of Technology, Melbourne, VIC 3122, Australia

3. Department of Infrastructure Engineering, University of Melbourne, Melbourne, VIC 3010, Australia

4. Institute of Railway Technology, Monash University, Melbourne, VIC 3800, Australia

5. Department of Civil and Construction Engineering, Swinburne University of Technology, Melbourne, VIC 3122, Australia

6. School of Business, Law and Entrepreneurship, Swinburne University of Technology, Melbourne, VIC 3122, Australia

7. School of Design and Architecture, Swinburne University of Technology, Melbourne, VIC 3122, Australia

Abstract

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.

Funder

Scope 3 Pty Ltd.

Swinburne University of Technology’s Research Office

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Link

https://www.mdpi.com/1424-8220/23/18/7971/pdf

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