Technology, data and standards

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Technology, data and standards

The effectiveness, scalability, and impact of the DIATOMIC initiative depend on a robust technological foundation built around open standards, flexible data models, and interoperable systems. By championing leading approaches to data exchange, semantic interoperability, and API design, the DIATOMIC platform supports seamless integration of digital twins across diverse domains such as urban infrastructure, mobility, and energy systems. This page outlines the core technological principles and standards that underpin DIATOMIC’s digital twin ecosystem, ensuring that applications can collaborate, scale, and adapt to evolving city requirements.

Key Concepts

Data Exchange Formats

Data exchange formats define the structure, serialization, and methods for sharing information between systems. In DIATOMIC:

JSON (JavaScript Object Notation) is predominantly used for its simplicity, efficiency, and wide adoption in IoT and smart city deployments. JSON is human- and machine-readable, highly performant, and easily integrates with APIs12.
CityGML and CityJSON are standard formats for representing 3D urban models, enabling complex geospatial data to be shared and visualized. CityJSON, in particular, offers a lightweight syntax suitable for API-driven architectures and is now favored for new digital twin services2.
GeoJSON is utilized for geospatial features such as points, lines, polygons, and more complex city geometries, supporting mapping, analytics, and spatial data integration2.
XML remains available for certain legacy or document-oriented data, but is less preferred due to its verbosity and weaker support for array-style data21.

Semantic Interoperability

Semantic interoperability ensures that data exchanged across different systems is not just machine-readable but also has unambiguous, shared meaning. This involves:

Ontologies: Collection of controlled vocabularies and relationships that describe domains such as sensors (SOSA/SSN), smart cities (SAREF4CITY), and energy systems, allowing for consistent annotation and understanding of data elements23 4.
Metadata and Context: Rich metadata schema describe source, measurement units, time, and spatial context, essential for accurate integration, querying, and decision-making52.
Standard Data Models: Use of recognized models such as NGSI-LD (by ETSI) for representing city entities, and Microsoft’s DTDL (Digital Twin Definition Language) for extensible digital twin descriptors, enables interoperability at both syntactic and semantic levels2.
Semantic Annotation: The process of linking raw data to standardized terms and ontologies, which is vital for discoverability and automated reasoning24.

APIs and Data Models

Standardized APIs (Application Programming Interfaces) allow different software components—be they digital twins, sensors, or analytics modules—to communicate and exchange data robustly and securely.
RESTful APIs and NGSI-LD APIs are widely adopted in DIATOMIC for accessing context-rich data and enabling plug-and-play integration of new services26.
Data Models provide consistent schemas for core entities (assets, measurements, locations), promoting data sharing between varied digital twin use cases (e.g., mobility, energy, environment)24.

Mechanisms

Implementing Data Exchange

APIs enforce consistent data formats: All public and internal APIs in DIATOMIC specify JSON, CityJSON, or GeoJSON payloads, ensuring a unified structure for requests and responses26.
Schema validation: Incoming and outgoing datasets are validated against open schemas and data models, such as those endorsed by OASC (Open Agile Smart Cities) and FIWARE’s Smart Data Models24.
Metadata enrichment: All data streams and records are tagged with standardized metadata (conforming to IEEE 2888.1-2023 for sensor data) for origin, timestamp, and measurement context2.

Achieving Semantic Interoperability

Ontology Alignment: Selection and alignment of ontologies, such as SAREF4CITY and SSN/SOSA, to ensure cross-domain understanding, with mapping services when custom extensions are needed24.
Context Brokering: NGSI-LD context broker services mediate data exchange and semantic queries among digital twins, using RESTful interfaces and JSON-LD serialization2.
Semantic Services: Middleware includes translation and annotation services that semantically enrich raw data and enable reasoning over federated datasets4.
Governance: Coordinated approaches for standard and domain-specific data models, managed in collaboration with partners and ecosystem stakeholders, mirroring best practice in data spaces4.

Extensible APIs and Data Models

APIs expose granular resources: Each type of digital twin (e.g., traffic, energy, hydrogen cells) defines endpoints that reflect its key entities, with consistent access patterns for retrieval, search, and updates26.
Open API documentation: All APIs publish their schemas using standards like OpenAPI or JSON Schema, reducing integration friction for new adopters26.
Plug-and-play compatibility: New data sources and digital twins can be registered by implementing or mapping to the agreed API and data model standards, enabling horizontal growth without central reengineering6.

Examples

Traffic and Air Quality Use Case: Data from hundreds of city-wide sensors is provided in JSON, annotated with air quality and timestamp metadata, and exchanged via NGSI-LD APIs. Semantic mapping allows integration of traffic data with pollution maps, supporting real-time analysis and route optimization26.
Energy Systems Digital Twin: Housing, energy network, and retrofit scenario data is shared as CityJSON for 3D visualization, and NGSI-LD-compliant JSON-LD for broader exchange. Ontologies such as SAREF4CITY and domain-specific models ensure interoperable insights across energy and built environment domains2.
Hydrogen Fuel Cell Twin: Predictive analytics modules access live and historical sensor data via standardized, RESTful endpoints, using common schemas for voltage, pressure, and degradation indicators. All expressions are semantically marked to enable automated analysis and reporting across the supply chain2.
International Collaboration: The Birmingham-Ulsan demonstrator aligns its digital twin data models to NGSI-LD and CityGML/CityJSON, facilitating seamless integration and test-sharing across international smart city testbeds2.
Accelerator SME Integration: Startups collaborating through the DIATOMIC accelerator leverage open APIs and published data models to quickly build, test, and deploy solutions, lowering barriers and enabling rapid scaling of innovation6.

Technology, data and standards

Technology, data and standards

Key Concepts

Data Exchange Formats

Semantic Interoperability

APIs and Data Models

Mechanisms

Implementing Data Exchange

Achieving Semantic Interoperability

Extensible APIs and Data Models

Examples

References

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