There is virtually no limit to what could be done when the data are combined using the right strategies, analytics methods and tools. The results could be both – the desired benefits or undesired consequences adversely affecting privacy, safety, fairness, and overall decisions. In our recent research on designing Digital Twins for IoT lifecycle data management in the Automotive industry (IoT4CPS project: https://iot4cps.at/), we aimed to enable retrospective data analysis by asking specific questions after the data is collected. To enable such analysis, we looked at relevant publicly available datasets to additionally extend the variety of data initially collected from use cases.

In this post, we investigate the IoT cross-platform interoperability landscape at the data level and look at its relevance for the Connected and Automated Mobility (CAM) applications. We consider the following two approaches for data interoperation:

Knowledge and data models, and initiatives based on the Semantic Web

• OASIS Open Service for Lifecycle Collaboration (OSLC) sets specifications for the integration of software development. These specification build on top of RDF (Resource Definition Framework), Linked Data and REST. The latest version of OSLC (Core 3.0) is based on W3C semantic standards, e.g. SHACL (Shape Constraint Language) (Knublauch and Ryman, 2019).
  • Relevance to CAM: OSLC provides standard adapters and connectors to a wide range of systems. It provides standardised mechanisms to support data federation and tool- and application-integration workflows (for lifecycle collaborative applications) in the cloud.
• ISO 15926: Industrial Automation Systems and Integration (Integration of Lifecycle Data for Process Plants Including Oil and Gas Production Facilities). This standard is created for data integration, data sharing and data exchange between different computer systems, and is available in OWL (Web Ontology Language).
  • Relevance to CAM: ISO 15926 is a data enabler and data integrator for OWL-based lifecycle data.
• ISO 10303-239 PLCS (Product Life Cycle Support), known also as the Standard for the Exchange of Product Model Data (STEP) and specifies information required for a product lifecycle.
  • Relevance to CAM: The latest version of STEP supports OWL data libraries and allows further extensions of existing industrial product data models towards cloud and IoT models.
• Cloud Information Model (CIM) supports connecting enterprise products through multiple cloud and on-premise applications. It is available in different formats: RDFS (RDF Schema), SHACL, R2RML, JSON-LD-based serialisation, JSON Schema, etc.
  • Relevance to CAM: It provides standardised, cloud-based data models for connecting enterprise products.
• The European Union Open Data Portal (EU ODP) is a catalogue of free to (re)use data for both commercial and noncommercial applications. The free data is available in different formats: RDF, SparQL and REST APIs. The data include geographic, geopolitical and financial data, statistics data, election results, legal acts, data on crime, health, the environment, transport and scientific research.
  • Relevance to CAM: It provides the free-to-use data.
• Relevant ontologies: We select the following potentially relevant ontologies:
  • SSNO (Semantic Sensor Networks Ontology) (https://www.w3.org/TR/vocab-ssn/) for describing sensors (their observations, procedures, features, samples, observed properties) and actuators.
  • SOSA (Sensor Observation Sampling Actuation) Ontology is a subset of the SSNO and includes a conceptualisation of all entities, activities and properties that typically constitute a Cyber Physical System (CPS) (https://www.w3.org/2015/spatial/wiki/SOSA_Ontology).
  • VSSo (Vehicle Signal and Attribute Ontology) provides a formal description of automotive attributes, vehicle signals and sensors. It is based on the Vehicle Signal Specification (GENIVI) and uses SSN/SOSA pattern for the description of signals (Klotz et al., 2018).
  • V2I (Vehicle to Infrastructure) WDI (Wireless Data Interface) Ontology (Venkata et al., 2019) is designed to support reasoning about new threats upon the addition of an unknown component to the system.
  • Thing Description (TD) Ontology describes the metadata and interfaces of Things that participate in the Web of Things. TD provides a set of interactions based on a small vocabulary that makes possible to integrate diverse devices and to allow diverse applications to interoperate (https://www.w3.org/TR/wot-thing-description/).
  • Relevance to CAM: SSN, SOSA and TD support a wide range of applications and use cases, including satellite imagery, monitoring, industrial and household infrastructures, social sensing, and the Web of Things (WoT), as fields of a wide interest to vehicle ecosystems. The reasoning features over threats and vulnerabilities of V2I applications are of the ultimate interest for CAM applications.
• Finally, Industrial Ontologies Foundry (IOF) is recent initiative to create a suite of open and principles-based reference ontologies, principles and best practices by which quality ontologies can be developed to support interoperability for various industrial domains (https://www.industrialontologies.org/).

Other initiatives for data sharing and data interoperation

• International Data Spaces Association (IDSA) (http://www.industrialdataspace.org/en/) envisions data exchange and communication through IDS Connectors and associated IDS Data Usage Constraints. IDS Connectors are software components that annotate data to be exchanged, with defined usage policies. IDS Connectors supports data sovereignty based on monitored data usage agreements between data providers and data consumers (Otto & Jarke, 2019).
• European Common Data Spaces, as part of the European Data Strategy: https://ec.europa.eu/digital-single-market/en/european-strategy-data
• Gaia-X project that promises a novel, federated data infrastructure for Europe (https://www.data-infrastructure.eu/GAIAX/Navigation/EN/Home/home.html).

More details are available from our public project report D5.3 “Cross-Platform Interoperation Model”.

References

Knublauch, H. and Ryman, A. (2019). Shapes Constraint Language (SHACL). Draft. Online available:
https://w3c.github.io/data-shapes/shacl/ Last accessed: November 25, 2019.

Klotz, B., Troncy, R., Wilms, D., Bonnet, C. (2018). VSSo: A Vehicle Signal and Attribute Ontology. In Proceedings of the 9th International Semantic Sensor Networks Workshop, Monterey, CA, USA

Otto & Jarke (2019). Otto, Boris and Jarke, Matthias, 2019. “Designing a multi-sided data platform: findings from the International Data Spaces case”. Electronic Markets, 2019. doi=”10.1007/s12525-019-00362-x

Gaia-X-pub1 (2019). Was ist das Projekt Gaia-X? Online available from:
https://www.bmwi.de/Redaktion/DE/FAQ/Dateninfrastruktur/faq-projekt-gaia-x-01.html Last accessed:
December 10, 2019.
Gaia-X-pub2 (2019). Eine vernetzte Dateninfrastruktur als Wiege eines vitalen, europäischen Ökosystems. Online available from: https://www.bmwi.de/Redaktion/DE/Artikel/Digitale-Welt/dateninfrastruktur.html, Last accessed: December 10, 2019