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Context-driven Edge-based Data Sharing for Industrial IoT Data Spaces

Abstract

The increasing reliance on IoT ecosystems demands robust, secure, and context-aware data-sharing mechanisms that operate closer to data sources. Data spaces must leverage trusted edge-based system architectures for near real-time data processing, transformation, and enrichment while ensuring data privacy and security. However, the current International Data Spaces (IDS) Model lacks comprehensive support for Edge-based architectures and flexible, context-driven access control models essential for managing diverse applications within data space ecosystems. To address these gaps, we propose IDS4Edge, an IDS-compliant approach that enables dynamic, context-driven, Edge-based IoT data sharing as a service. IDS4Edge integrates flexible access control policies on top of IDS connectors, tailored to specific IoT application contexts. These policies dynamically adapt in real-time to changes in IoT contexts and contractual agreements, ensuring secure and efficient data sharing at the Edge. We validate our solution through a proof-of-concept implementation, demonstrating how IDS4Edge facilitates trusted, scalable, and real-time data sharing while maintaining compliance with IDS principles. This approach paves the way for enhanced (industrial) IoT applications and advanced data-sharing paradigms, such as Manufacturing-as-a-Service (MaaS).
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Category

Academic chapter

Language

English

Affiliation

  • SINTEF Industry / Metal Production and Processing
  • SINTEF Digital / Sustainable Communication Technologies

Year

2025

Publisher

Association for Computing Machinery (ACM)

Book

SAC '25: Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing

ISBN

9798400706295

Page(s)

498 - 505

View this publication at Norwegian Research Information Repository