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Reshaping smart cities through NGSI-LD enrichment

Abstract: The vast amount of information stemming from the deployment of the Internet of Things and open data portals is poised to provide significant benefits for both the private and public sectors, such as the development of value-added services or an increase in the efficiency of public services. This is further enhanced due to the potential of semantic information models such as NGSI-LD, which enable the enrichment and linkage of semantic data, strengthened by the contextual information present by definition. In this scenario, advanced data processing techniques need to be defined and developed for the processing of harmonised datasets and data streams. Our work is based on a structured approach that leverages the principles of linked-data modelling and semantics, as well as a data enrichment toolchain framework developed around NGSI-LD. Within this framework, we reveal the potential for enrichment and linkage techniques to reshape how data are exploited in smart cities, with a particular focus on citizen-centred initiatives. Moreover, we showcase the effectiveness of these data processing techniques through specific examples of entity transformations. The findings, which focus on improving data comprehension and bolstering smart city advancements, set the stage for the future exploration and refinement of the symbiosis between semantic data and smart city ecosystems.

 Authorship: González V., Martín L., Santana J.R., Sotres P., Lanza J., Sánchez L.,

 Fuente: Sensors 2024, 24(6), 1858

 Publisher: MDPI

 Publication date: 14/03/2024

 No. of pages: 16

 Publication type: Article

 DOI: 10.3390/s24061858

 ISSN: 1424-8220

 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/