Harnessing Data Spaces to Build Intelligent Smart City Infrastructures Across the Cloud-Edge Continuum

TL;DR

This work addresses the challenge of building scalable, privacy-preserving smart cities by combining sovereign data spaces with a cloud–edge continuum. It生命周期 demonstrates an office-building climate-monitoring use case that leverages edge analytics, federated data sharing, and semantically described data sources within an AC$^3$ platform. The key contributions include integrating IDS-based blueprints (IDS-RAM, IDS Connector), Gaia-X governance, and FIWARE NGSI-LD into a DSP-driven data-space fabric, deploying edge-to-cloud AI services for anomaly detection, forecasting, and occupancy sensing, and evaluating the approach on multi-source smart-building datasets. The results show robust anomaly detection, competitive forecasting accuracy, and energy-aware occupancy insights, offering a practical blueprint for data-centric, interoperable smart city deployments with data sovereignty at the core.

Abstract

Smart cities are increasingly adopting data-centric architectures to enhance the efficiency, sustainability, and resilience of urban services.

Figures (4)

• Figure 1: Schematic representation of the smart building application's deployment using $AC^3$ and our deployed data source.
• Figure 2: The smart building application's room list page.
• Figure 3: The smart building application's room information page.
• Figure 4: The smart building application's sensor data view page.