IoT and machine learning for enabling sustainable development goals

Jaber, Mona

doi:10.3389/frcmn.2023.1219047

Cited by 8 publications

(2 citation statements)

References 58 publications

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“…Recent advancements have highlighted the role of the Internet of Things (IoTs) and machine learning in achieving the SDGs, with use cases in health, energy, and cities [25]. Furthermore, the application of Deep Graph Learning (DGL) has been proposed to address societal challenges and improve people's daily lives [26].…”

Section: Ai DL and Sustainable Development Goals (Sdgs)mentioning

confidence: 99%

Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health

Fan,

Yan,

Wen

2023

Sustainability

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Artificial intelligence (AI) and deep learning (DL) have shown tremendous potential in driving sustainability across various sectors. This paper reviews recent advancements in AI and DL and explores their applications in achieving sustainable development goals (SDGs), renewable energy, environmental health, and smart building energy management. AI has the potential to contribute to 134 of the 169 targets across all SDGs, but the rapid development of these technologies necessitates comprehensive regulatory oversight to ensure transparency, safety, and ethical standards. In the renewable energy sector, AI and DL have been effectively utilized in optimizing energy management, fault detection, and power grid stability. They have also demonstrated promise in enhancing waste management and predictive analysis in photovoltaic power plants. In the field of environmental health, the integration of AI and DL has facilitated the analysis of complex spatial data, improving exposure modeling and disease prediction. However, challenges such as the explainability and transparency of AI and DL models, the scalability and high dimensionality of data, the integration with next-generation wireless networks, and ethics and privacy concerns need to be addressed. Future research should focus on enhancing the explainability and transparency of AI and DL models, developing scalable algorithms for processing large datasets, exploring the integration of AI with next-generation wireless networks, and addressing ethical and privacy considerations. Additionally, improving the energy efficiency of AI and DL models is crucial to ensure the sustainable use of these technologies. By addressing these challenges and fostering responsible and innovative use, AI and DL can significantly contribute to a more sustainable future.

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Section: Ai DL and Sustainable Development Goals (Sdgs)mentioning

confidence: 99%

Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health

Fan,

Yan,

Wen

2023

Sustainability

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“…Various enabling technologies can be adopted for this purpose, including spatial data management and machine learning (ML) techniques, combined with the collection of data from Internet of Things (IoT) devices [9][10][11] and satellite infrastructure [12,13]. The combination of IoT and ML technologies can provide solutions that contribute to local monitoring and assessment observatories for the SDGs.…”

Section: Introductionmentioning

confidence: 99%

A Knowledge Graph-Driven Analysis of the Interlinkages among the Sustainable Development Goal Indicators in Different Spatial Resolutions

Androna,

Mandilara,

Fotopoulou

et al. 2024

Sustainability

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The way towards sustainable development is paved through the commitment to the 17 Sustainable Development Goals (SDGs), which encompass a wide range of global challenges. The successful progress of these goals depends on the identification and understanding of their interconnected nature. A plethora of data is made available for tracking targets related to the SDGs at country, regional and urban levels. However, various challenges are identified to semantically align and homogeneously represent such data to improve their interoperability, comparability and analysis. In the current work, we provide an innovative solution for analyzing SDG-oriented data based on the development of a Knowledge Graph that provides access to semantically aligned data for the SDGs. We consider Knowledge Graphs as a suitable technology for the representation of data related to the interlinkages among SDGs, since they provide a structured representation of knowledge that incorporates entities, relationships and attributes, organized in a graph format. We examine the interlinkages among indicators of the same SDG, as well as across indicators of the various SDGs. Such interlinkages are further evaluated as synergies or trade-offs. Our analysis is applied in country and regional levels, considering various constraints in terms of data quality and availability. In total 476 synergies are identified at the national level among the SDGs, compared to 140 trade-offs. The SDGs that mostly participate in the synergies are SDGs 17, 10, 9 and 8, while SDGs 7 and 16 participate in most of the trade-offs. At the regional level, SDGs 8, 4 and 9 are more active in terms of interlinkages.

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Impact of Dimethylformamide, Tetrahydrofuran, and Dimethyl Sulfoxide on Bulk Heterojunction Organic Solar Cells’ Efficiency and Environmental Footprint

Rodríguez‐Mas,

Valiente,

Corral

et al. 2024

Adv Energy and Sustain Res

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Organic solar cells (OSCs), especially those employing bulk heterojunction architecture, present a promising avenue in renewable energy technology. These devices utilize organic materials and can be doped by solvents such as dimethylformamide (DMF), tetrahydrofuran (THF), and dimethyl sulfoxide (DMSO). Solvent doping (DMF, THF, and DMSO) is observed to augment the efficiency of OSCs. However, a trade‐off exists between the volume of solvent used and the device's efficiency. The judicious selection of solvents is crucial as it directly impacts the environmental footprint of the fabrication process and the power conversion efficiency. Notably, the use of solvents in OSC fabrication contributes to reducing the environmental impact across various categories, in particular Abiotic Depletion, Global Warming, and Human Toxicity. Among the solvents studied, THF demonstrates the most signiﬁcant reduction in environmental impact. Therefore, optimizing the choice and volume of solvents in OSC fabrication is paramount for achieving both enhanced device performance and minimal environmental footprint.

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IoT and machine learning for enabling sustainable development goals

Cited by 8 publications

References 58 publications

Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health

Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health

A Knowledge Graph-Driven Analysis of the Interlinkages among the Sustainable Development Goal Indicators in Different Spatial Resolutions

Impact of Dimethylformamide, Tetrahydrofuran, and Dimethyl Sulfoxide on Bulk Heterojunction Organic Solar Cells’ Efficiency and Environmental Footprint

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