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Reshaping Smart Cities through NGSI-LD Enrichment.

Víctor González1, Laura Martín1, Juan Ramón Santana1

  • 1Network Planning and Mobile Communications Laboratory, Universidad de Cantabria, 39005 Santander, Spain.

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|March 28, 2024
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Summary
This summary is machine-generated.

Leveraging semantic information models like NGSI-LD, this study enhances smart city data processing. Advanced techniques improve data comprehension and linkage, benefiting citizen-centered initiatives and public services.

Keywords:
data enrichmentdata processingdata understandabilitylinked datasemantic annotationsmart cities

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Area of Science:

  • Computer Science
  • Urban Informatics
  • Data Science

Background:

  • The proliferation of Internet of Things (IoT) data and open data portals offers significant potential for public and private sectors.
  • Semantic information models, such as NGSI-LD, enhance data value through enrichment and linkage, leveraging inherent contextual information.
  • Advanced data processing techniques are crucial for harmonizing and analyzing these complex datasets and data streams.

Purpose of the Study:

  • To define and develop advanced data processing techniques for harmonized datasets and data streams within smart city ecosystems.
  • To explore the potential of data enrichment and linkage techniques to transform data exploitation in smart cities.
  • To focus on citizen-centered initiatives and demonstrate the effectiveness of these techniques through practical examples.

Main Methods:

  • Utilizing a structured approach based on linked-data modeling and semantics.
  • Developing a data enrichment toolchain framework centered around NGSI-LD.
  • Showcasing entity transformations to illustrate data processing capabilities.

Main Results:

  • Demonstrated the effectiveness of NGSI-LD based data enrichment and linkage techniques.
  • Revealed the potential to reshape data exploitation for smart city applications, particularly for citizen-centered initiatives.
  • Provided specific examples of entity transformations highlighting improved data processing.

Conclusions:

  • The proposed data processing techniques significantly improve data comprehension in smart city environments.
  • Findings support the advancement of smart city initiatives through enhanced semantic data utilization.
  • This work lays the foundation for future research into the synergy between semantic data and smart city ecosystems.