SEDIA: A Platform for Semantically Enriched IoT Data Integration and Development of Smart City Applications

Lymperis, Dimitrios; Goumopoulos, Christos

doi:10.3390/fi15080276

Cited by 5 publications

(3 citation statements)

References 54 publications

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“…Finally, Ref. [ 41 ] introduces a platform for developing smart applications based on diverse data sources to support a semantically enriched data model for effective data analysis and integration. This platform also includes a semantic layer that takes the gathered data and represents it utilising semantic web technologies for the annotation and linking of data, and to facilitate more accurate and meaningful analysis.…”

Section: Related Workmentioning

confidence: 99%

Reshaping Smart Cities through NGSI-LD Enrichment

González,

Martín,

Santana

et al. 2024

Sensors

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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.

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Section: Related Workmentioning

confidence: 99%

Reshaping Smart Cities through NGSI-LD Enrichment

González,

Martín,

Santana

et al. 2024

Sensors

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“…This middleware is based on intelligent agents and uses ontologies for the adaptation to the specific context and to respond to unforeseen situations. The authors of [48] presented a platform for the integration of the diverse and heterogeneous data sources of a Smart City, capable of also incorporating geographical data and supporting a semantically enriched data model by means of ontology classes and properties for effective data analysis and integration. Moreover, the platform facilitates the deployment of Smart Cities applications, offering a suite of high-level services, which can be repurposed by developers.…”

Section: Related Workmentioning

confidence: 99%

An Urban Intelligence Architecture for Heterogeneous Data and Application Integration, Deployment and Orchestration

Silvestri,

Tricomi,

Bassolillo

et al. 2024

Sensors

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This paper describes a novel architecture that aims to create a template for the implementation of an IT platform, supporting the deployment and integration of the different digital twin subsystems that compose a complex urban intelligence system. In more detail, the proposed Smart City IT architecture has the following main purposes: (i) facilitating the deployment of the subsystems in a cloud environment; (ii) effectively storing, integrating, managing, and sharing the huge amount of heterogeneous data acquired and produced by each subsystem, using a data lake; (iii) supporting data exchange and sharing; (iv) managing and executing workflows, to automatically coordinate and run processes; and (v) to provide and visualize the required information. A prototype of the proposed IT solution was implemented leveraging open-source frameworks and technologies, to test its functionalities and performance. The results of the tests performed in real-world settings confirmed that the proposed architecture could efficiently and easily support the deployment and integration of heterogeneous subsystems, allowing them to share and integrate their data and to select, extract, and visualize the information required by a user, as well as promoting the integration with other external systems, and defining and executing workflows to orchestrate the various subsystems involved in complex analyses and processes.

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“…Another work, [50], was proposed in this area, with a prime focus on resolving privacy and security issues, specifically concentrated on the message queuing telemetry transport (MQTT) protocol. Some of the recent works that have amalgamated the concept of semantic annotations in IoT middleware include GMSCA [51], S2NetM [52], and SEDIA [53]. GMSCA is a generic IoT middleware for smart city applications that targets to address challenges regarding heterogeneity, service management, and security.…”

Section: High Complexity and Data Redundancymentioning

confidence: 99%

A Novel Semantic IoT Middleware for Secure Data Management: Blockchain and AI-Driven Context Awareness

Elkhodr,

Khan,

Gide

2024

Future Internet

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In the modern digital landscape of the Internet of Things (IoT), data interoperability and heterogeneity present critical challenges, particularly with the increasing complexity of IoT systems and networks. Addressing these challenges, while ensuring data security and user trust, is pivotal. This paper proposes a novel Semantic IoT Middleware (SIM) for healthcare. The architecture of this middleware comprises the following main processes: data generation, semantic annotation, security encryption, and semantic operations. The data generation module facilitates seamless data and event sourcing, while the Semantic Annotation Component assigns structured vocabulary for uniformity. SIM adopts blockchain technology to provide enhanced data security, and its layered approach ensures robust interoperability and intuitive user-centric operations for IoT systems. The security encryption module offers data protection, and the semantic operations module underpins data processing and integration. A distinctive feature of this middleware is its proficiency in service integration, leveraging semantic descriptions augmented by user feedback. Additionally, SIM integrates artificial intelligence (AI) feedback mechanisms to continuously refine and optimise the middleware’s operational efficiency.

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SEDIA: A Platform for Semantically Enriched IoT Data Integration and Development of Smart City Applications

Cited by 5 publications

References 54 publications

Reshaping Smart Cities through NGSI-LD Enrichment

Reshaping Smart Cities through NGSI-LD Enrichment

An Urban Intelligence Architecture for Heterogeneous Data and Application Integration, Deployment and Orchestration

A Novel Semantic IoT Middleware for Secure Data Management: Blockchain and AI-Driven Context Awareness

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