Waste Management Through Digital Twins and Business Process Modeling

Littering is an environmental problem that affects citizens' economy, safety, and health. Natural and rural areas are often targets of abandoned littering, while urban areas often accumulate more waste than can be disposed of in a timely manner. Minimizing littering and waste is a critical sustainability challenge requiring the cooperation of different professionals and agencies. In this paper, we report our vision and preliminary proposal for a model-driven approach to address the automated localization and identification of abandoned waste. Our solution envisages the usage of digital process twins to enable the specification of cost-effective and self-adaptive procedures fed by data crowdsourced from the real world.

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Publication Details

Published
2024-09-22
DOI
https://doi.org/10.1145/3652620.3687796
Citations
4
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
1.40
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article

Waste Management Through Digital Twins and Business Process Modeling

Amleto Di Salle, Daniela Micucci, Arianna Fedeli, Ludovico Iovino et al.
4 citations
Digital Transformation in Industry
1.40
article

Waste Management Through Digital Twins and Business Process Modeling

Amleto Di Salle, Daniela Micucci, Arianna Fedeli, Ludovico Iovino, Leonardo Mariani, Luciana Brasil Rebelo dos Santos, Maria Teresa Rossi
article en
4 citations

Abstract

Littering is an environmental problem that affects citizens' economy, safety, and health. Natural and rural areas are often targets of abandoned littering, while urban areas often accumulate more waste than can be disposed of in a timely manner. Minimizing littering and waste is a critical sustainability challenge requiring the cooperation of different professionals and agencies. In this paper, we report our vision and preliminary proposal for a model-driven approach to address the automated localization and identification of abandoned waste. Our solution envisages the usage of digital process twins to enable the specification of cost-effective and self-adaptive procedures fed by data crowdsourced from the real world.

Gran Sasso Science Institute (IT), University of Milano-Bicocca (IT)
Openalex Percentile: Top 16%
Digital Transformation in Industry
1.40
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Waste Management Through Digital Twins and Business Process Modeling — Amleto Di Salle, Daniela Micucci, et al. · (2024) | TGRS Research Map | TGRS