Toward Greener Smart Cities: A Critical Review of Classic and Machine-Learning-Based Algorithms for Smart Bin Collection
This study critically reviews the scientific literature regarding machine-learning approaches for optimizing smart bin collection in urban environments. Usually, the problem is modeled within a dynamic graph framework, where each smart bin’s changing waste level is represented as a node. Algorithms...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-02-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/13/5/836 |
| _version_ | 1797264666593853440 |
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| author | Alice Gatti Enrico Barbierato Andrea Pozzi |
| author_facet | Alice Gatti Enrico Barbierato Andrea Pozzi |
| author_sort | Alice Gatti |
| collection | DOAJ |
| description | This study critically reviews the scientific literature regarding machine-learning approaches for optimizing smart bin collection in urban environments. Usually, the problem is modeled within a dynamic graph framework, where each smart bin’s changing waste level is represented as a node. Algorithms incorporating Reinforcement Learning (RL), time-series forecasting, and Genetic Algorithms (GA) alongside Graph Neural Networks (GNNs) are analyzed to enhance collection efficiency. While individual methodologies present limitations in computational demand and adaptability, their synergistic application offers a holistic solution. From a theoretical point of view, we expect that the GNN-RL model dynamically adapts to real-time data, the GNN-time series predicts future bin statuses, and the GNN-GA hybrid optimizes network configurations for accurate predictions, collectively enhancing waste management efficiency in smart cities. |
| first_indexed | 2024-04-25T00:32:32Z |
| format | Article |
| id | doaj.art-b711edb3ee444282ba68570536f4e645 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-25T00:32:32Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-b711edb3ee444282ba68570536f4e6452024-03-12T16:42:19ZengMDPI AGElectronics2079-92922024-02-0113583610.3390/electronics13050836Toward Greener Smart Cities: A Critical Review of Classic and Machine-Learning-Based Algorithms for Smart Bin CollectionAlice Gatti0Enrico Barbierato1Andrea Pozzi2Department of Mathematics and Physics, Catholic University of the Sacred Heart, via Garzetta 48, 25133 Brescia, ItalyDepartment of Mathematics and Physics, Catholic University of the Sacred Heart, via Garzetta 48, 25133 Brescia, ItalyDepartment of Mathematics and Physics, Catholic University of the Sacred Heart, via Garzetta 48, 25133 Brescia, ItalyThis study critically reviews the scientific literature regarding machine-learning approaches for optimizing smart bin collection in urban environments. Usually, the problem is modeled within a dynamic graph framework, where each smart bin’s changing waste level is represented as a node. Algorithms incorporating Reinforcement Learning (RL), time-series forecasting, and Genetic Algorithms (GA) alongside Graph Neural Networks (GNNs) are analyzed to enhance collection efficiency. While individual methodologies present limitations in computational demand and adaptability, their synergistic application offers a holistic solution. From a theoretical point of view, we expect that the GNN-RL model dynamically adapts to real-time data, the GNN-time series predicts future bin statuses, and the GNN-GA hybrid optimizes network configurations for accurate predictions, collectively enhancing waste management efficiency in smart cities.https://www.mdpi.com/2079-9292/13/5/836smart binsroutinggraph neural networkshybrid models |
| spellingShingle | Alice Gatti Enrico Barbierato Andrea Pozzi Toward Greener Smart Cities: A Critical Review of Classic and Machine-Learning-Based Algorithms for Smart Bin Collection Electronics smart bins routing graph neural networks hybrid models |
| title | Toward Greener Smart Cities: A Critical Review of Classic and Machine-Learning-Based Algorithms for Smart Bin Collection |
| title_full | Toward Greener Smart Cities: A Critical Review of Classic and Machine-Learning-Based Algorithms for Smart Bin Collection |
| title_fullStr | Toward Greener Smart Cities: A Critical Review of Classic and Machine-Learning-Based Algorithms for Smart Bin Collection |
| title_full_unstemmed | Toward Greener Smart Cities: A Critical Review of Classic and Machine-Learning-Based Algorithms for Smart Bin Collection |
| title_short | Toward Greener Smart Cities: A Critical Review of Classic and Machine-Learning-Based Algorithms for Smart Bin Collection |
| title_sort | toward greener smart cities a critical review of classic and machine learning based algorithms for smart bin collection |
| topic | smart bins routing graph neural networks hybrid models |
| url | https://www.mdpi.com/2079-9292/13/5/836 |
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