Report Guidance for the Integration of IoT and Edge Computing in Data Spaces

AIOTI WG Standardisation Sub-Group High Level Architecture prepared a report on Guidance for the Integration of IoT and Edge Computing in Data Spaces.

The full report can be found here.

This document provides an analysis on the integration of IoT and edge computing in data spaces.

It explains the context, providing a definition of data spaces, enumerating challenges of data spaces, as well as the positioning of data spaces in the AIOTI high-level architecture (HLA).

It provides an architecture analysis of data spaces, covering:

• data space systems of interest from three perspectives: computing continuum, federation of systems, and data collecting / trading;
• stakeholders of data space systems;
• concerns and properties, general to data spaces, specific to cyber physical systems, to the integration of edge computing and processing, and to trustworthiness;
• building blocks to address concerns related to data governance, cyber physical systems and digital twins, trustworthiness support, interoperability support, infrastructure reconfiguration support, and data business marketplaces.

It describes the relation to existing solutions:

• a construction approach relying on reference architecture standards and patterns:
• the use of reference architectures proposal from IDSA, oneM2M, ETSI MEC;
• the work carried out by a number of large-scale projects: PLATOON, INTERCONNECT, SmartBear, ASSIST-IoT.

It provides recommendations for data space standards.

This report has provided an analysis on the integration of IoT and edge computing in data spaces. Three recommendations are made:

The first recommendation is to agree on data space principles. This paper has identified 12 principles, detailed in Table 1 and summarised in Table 23.

Table 25  – Twelve data space principles

The second recommendation is to work on data space standards following an architecture of standard as showed in Figure 23:

• Use cases justify concepts, processes, architectures, interoperability, systems. Data space use case standards provide an inventory of application needs that can be used to justify other standards.
• Concepts enable specification of processes, architectures, interoperability, systems. Data space concept standards (terms, principles, ontologies, frameworks) can be used to enable the specification of process, architecture, interoperability and system standards.
• Processes support the construction of architectures, interoperability, systems. Data space process standards can include management, engineering, conformity assessment, continuity management.
• Architectures and Interoperability support the construction and integration of systems
  • Data space architecture standards provide means to build high-level specifications of systems.
  • Data space Interoperability standards provide means to enable exchange of information, or service provisions by systems.
• System support integration into systems of systems, ecosystems. Data space system standards provide means to construct and operate systems of systems (ecosystems)

The third recommendation is to integrate IoT, Edge and digital twin concerns in data space standards. Note that the standards should be jointly worked out by working groups focusing on AI, on data, on data governance, on IoT, on CPS and on digital twins.