Abstract: In the era of burgeoning data diversity in heterogeneous sources, unlocking valuable insights
becomes pivotal. Raw data often lack context and meaning, necessitating the deployment of
services that link and enhance data, thereby extracting meaningful patterns and information.
For example, exploring the significance of IoT sensors in measuring air quality across cities
emphasizes the potential to establish connections between air quality and associated metrics
like traffic intensity and meteorological conditions.
Introducing the Data Enrichment Toolchain (DET), this study underscores its role in
harmonizing and curating diverse datasets. DET operates on linked-data principles and adheres
to the NGSI-LD standard, enabling seamless integration and correlation analysis across disparate
data domains. The research delves into the intricate relationship between traffic patterns and
prevalent air pollutants, utilizing enriched datasets from European cities focusing on the smart
city of Madrid as a use-case.
Considering the COVID-19 pandemic?s impact on traffic flow and meteorological influences
on air quality, the study examines pre-pandemic, pandemic, and post-pandemic traffic scenarios
in Madrid. By leveraging DET-enhanced datasets, the investigation aims to unravel nuanced
insights into the interplay between traffic, meteorological factors, and air quality, offering
valuable implications for urban planning and pollution mitigation strategies.
Fuente: Internet of Things, 2024, 26, 101232
Publisher: Elsevier
Publication date: 01/07/2024
No. of pages: 20
Publication type: Article
DOI: 10.1016/j.iot.2024.101232
ISSN: 2542-6605,2543-1536
Spanish project: TED2021-131988B-I00
European project: info:eu-repo/grantAgreement/EC/CEF/2020-EU-IA-0274/EU/Situation-Aware Linked heTerogeneous Enriched Data /SALTED/
Publication Url: https://doi.org/10.1016/j.iot.2024.101232