Standards Development

Scope

This document provides guidance on the connection of IoT systems and digital twins in data spaces, including principles, architecture and lifecycle considerations. It is based upon

- concepts and characteristics described in ISO/IEC 20151 (Dataspace concepts and characteristics),

- architecture considerations described in ISO/IEC 30141 Ed2 (IoT reference architecture) and ISO/IEC 30188 (Digital twin reference architecture), and interoperability aspects described in the ISO/IEC 21823 series (Interoperability for IoT systems).

Purpose

Data (information and knowledge is widely viewed as the next factor in the evolution of the economy.
While the term data space was coined more than 10 years ago (https://en.wikipedia.org/wiki/Dataspaces), it
was not until recent years that a number of position papers, and initiatives, have started to propose a common
understanding:
• Towards a European-Governed Data Sharing Space. Enabling data exchange and unlocking AI
potential. April 2019,
https://bdva.eu/sites/default/files/BDVA%20DataSharingSpace%20PositionPaper_April2019_V1.pdf
• Towards a European-Governed Data Sharing Space. Enabling data exchange and unlocking AI
potential. November 2020,
https://www.bdva.eu/sites/default/files/BDVA%20DataSharingSpaces%20PositionPaper%20V2_2020_Final .pdf
• Design principles for data spaces, https://design-principles-for-data-spaces.org/
• IDS Reference architecture model, https://www.internationaldataspaces.org/wpcontent/uploads/2019/03/IDS-Reference-Architecture-Model-3.0.pdf
• GAIA-X technical architecture, https://www.datainfrastructure.eu/GAIAX/Redaktion/EN/Publications/gaia-x-technicalarchitecture.pdf?__blob=publicationFile&v=5. Release-June 2020, https://www.datainfrastructure.eu/GAIAX/Redaktion/EN/Publications/gaia-x-technical-architecture.pdf
• City data spaces: A guide to building and operationalising data services,
https://www.fiware.org/marketing-toolbox/city-data-spaces-a-guide-to-building-and-operationalising-dataservices/

From a technical perspective, a data space can be seen as a data integration concept which does not require
common database schemas and physical data integration, but is rather based on distributed data stores and
integration on an “as needed” basis on a semantic level. Abstracted from this technical definition, a data
space can be defined as a federated data ecosystem within a certain application domain and based on
shared policies and rules.
It is expected that data spaces will integrate the edge and IoT, and digital twins. As stated in a recent study
(https://www.gartner.com/smarterwithgartner/what-edge-computing-means-for-infrastructure-andoperations-leaders), around 10% of enterprise-generated data is created and processed outside a traditional
centralized data center or cloud. By 2025, this figure will reach 75%.
There is therefore a need to provide quidance on two integration issues:
• integrating data produced by IoT systems as explained in the following report AIOTI report: ‘Guidance
for the Integration of IoT and Edge Computing in Data Spaces. ISO/IEC JTC1/SC41 N2160. September