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. 2024 Mar 14;24(6):1858.
doi: 10.3390/s24061858.

Reshaping Smart Cities through NGSI-LD Enrichment

Víctor González  1 , Laura Martín  1 , Juan Ramón Santana  1 , Pablo Sotres  1 , Jorge Lanza  1 , Luis Sánchez  1
Affiliations

Reshaping Smart Cities through NGSI-LD Enrichment

Víctor González et al. Sensors (Basel). .

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.

Keywords: data enrichment; data processing; data understandability; linked data; semantic annotation; smart cities.

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Conflict of interest statement

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Functional architecture of the data enrichment toolchain (source: [10]).
Figure 2
Figure 2
IoT data linker, high-level view.
Figure 3
Figure 3
Geolocation data linker, high-level view.
Figure 4
Figure 4
Reverse geocoding data enricher, high-level view.
Figure 5
Figure 5
Insight data enricher, high-level view.
Figure 6
Figure 6
IoT data linker entity enhancement.
Figure 7
Figure 7
Geolocation data linker entity enhancement.
Figure 8
Figure 8
Reverse geocoding data enricher entity enhancement.
Figure 9
Figure 9
Insight data enricher entity enhancement.
See this image and copyright information in PMC

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