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

被引:6
|
作者
Yavari, Ali [1 ,2 ]
Mirza, Irfan Baig [1 ]
Bagha, Hamid [3 ]
Korala, Harindu [4 ]
Dia, Hussein [5 ]
Scifleet, Paul [6 ]
Sargent, Jason [6 ]
Tjung, Caroline [7 ]
Shafiei, Mahnaz [1 ]
机构
[1] Swinburne Univ Technol, Sch Sci Comp & Engn Technol, Melbourne, Vic 3122, Australia
[2] Swinburne Univ Technol, Res & Innovat Lab 6G, Melbourne, Vic 3122, Australia
[3] Univ Melbourne, Dept Infrastructure Engn, Melbourne, Vic 3010, Australia
[4] Monash Univ, Inst Railway Technol, Melbourne, Vic 3800, Australia
[5] Swinburne Univ Technol, Dept Civil & Construction Engn, Melbourne, Vic 3122, Australia
[6] Swinburne Univ Technol, Sch Business Law & Entrepreneurship, Melbourne, Vic 3122, Australia
[7] Swinburne Univ Technol, Sch Design & Architecture, Melbourne, Vic 3122, Australia
关键词
IoT; greenhouse gas; sustainable logistics; emissions; supply chain; AI; PREDICTION;
D O I
10.3390/s23187971
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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.
引用
收藏
页数:17
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