Crowdsourcing by IoT using LabVIEW for Measuring the Air Quality

Khaïli, Mohamed El; Bakkoury, Jamila; Khiat, Azeddine; Alloubane, Abdelkarim

doi:10.1145/3286606.3286837

Cited by 3 publications

(3 citation statements)

References 1 publication

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“…The V2I network's role is send short messages within its range containing the same information as the current traffic signals, but under the central unit control in real-time. If the vehicle receives real-time alerts about road incidents, they could create dynamic maps to reevaluate and plan their routes to meet some required criteria [20,21,22].…”

Section: Data Collection Processmentioning

confidence: 99%

“…The air quality measurement is traditionally based on the deployment by the local authority of fixed sensors in the city. Several initiatives have emerged at the intersection of the citizen sensors and the Open Hardware, to design low cost sensors increase coupled smartphones to enable a collaborative data collection (crowdsourcing) and so multiply the observations and measurements as illustrated in Figure 3 [21,2]. All tracers should be connected to the Treatment Unit, so that the data could be collected and processed.…”

Section: Data Collection Processmentioning

confidence: 99%

“…The index of air quality is computed per each region who correspond to it tracer. The indices can be displayed on a map of the city or used for regulating traffics and other way of management for the city's facilities [18,20,21].…”

Section: Data Collection Processmentioning

confidence: 99%

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Towards a Green Supply Chain Based on Smart Urban Traffic Using Deep Learning Approach

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Green Supply Chain Management (GrSCM) has become one of the most crucial innovation in the Supply Chain Management (SCM). This approach involves environmental concerns and issues into the SCM, thus, companies and authorities tend to exploit the GrSCM through logistics process in order to improve their performance. In this paper, we will give a demonstration of the added value of the Urban Traffific Management (UTM) and its integration in the concept of GrSCM, we also aim to study its impact on the performance improvement in Transport Management with a focus on Air quality improvement. This study proposes a new approach and model based on Deep learning for Urban Traffific Control Management to solve the traffific flflow problem in order to reduce the congestion, improve the air quality and enhance the urban supply chain. Our proposed framework for Data collection and processing is mainly based on Internet of Thing (IoT) technologies for an effificient Smart City.

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Section: Data Collection Processmentioning

confidence: 99%

Section: Data Collection Processmentioning

confidence: 99%

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Towards a Green Supply Chain Based on Smart Urban Traffic Using Deep Learning Approach

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Terrada

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2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS)

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Supervised Machine Learning Approaches for Predicting Key Pollutants and for the Sustainable Enhancement of Urban Air Quality: A Systematic Review

Essamlali,

Nhaila,

El Khaili

2024

Sustainability

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Urban air pollution is a pressing global issue driven by factors such as swift urbanization, population expansion, and heightened industrial activities. To address this challenge, the integration of Machine Learning (ML) into smart cities presents a promising avenue. Our article offers comprehensive insights into recent advancements in air quality research, employing the PRISMA method as a cornerstone for the reviewing process, while simultaneously exploring the application of frequently employed ML methodologies. Focusing on supervised learning algorithms, the study meticulously analyzes air quality data, elucidating their unique benefits and challenges. These frequently employed ML techniques, including LSTM (Long Short-Term Memory), RF (Random Forest), ANN (Artificial Neural Networks), and SVR (Support Vector Regression), are instrumental in our quest for cleaner, healthier urban environments. By accurately predicting key pollutants such as particulate matter (PM), nitrogen oxides (NOx), carbon monoxide (CO), and ozone (O3), these methods offer tangible solutions for society. They enable informed decision-making for urban planners and policymakers, leading to proactive, sustainable strategies to combat urban air pollution. As a result, the well-being and health of urban populations are significantly improved. In this revised abstract, the importance of frequently employed ML methods in the context of air quality is explicitly emphasized, underlining their role in improving urban environments and enhancing the well-being of urban populations.

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Crowdsourcing by IoT using LabVIEW for Measuring the Air Quality

Cited by 3 publications

References 1 publication

Towards a Green Supply Chain Based on Smart Urban Traffic Using Deep Learning Approach

Towards a Green Supply Chain Based on Smart Urban Traffic Using Deep Learning Approach

Smart Urban Traffic for Green Supply Chain Management

Supervised Machine Learning Approaches for Predicting Key Pollutants and for the Sustainable Enhancement of Urban Air Quality: A Systematic Review

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