Design and Implementation of a FIWARE-Based Education Smart Data Model for University Campus Management

Abstract

Smart campus development is increasingly associated with the combined use of IoT technologies, artificial intelligence, cloud infrastructures, and large-scale data analytics in higher education. Despite this progress, many existing data models are not well-suited to the educational domain, particularly when interoperability and real-time analytical capabilities are required. To address this limitation, the study proposes a Smart Campus Education Data Model (SCEDM), which can be integrated into any FIWARE-based platform. The model is organized as a layered architecture that includes data acquisition, processing, and storage; analytics and decision support; application presentation; and security. The proposed model is not presented only at a conceptual level; it is also validated in a con-tainerized FIWARE environment built around the Orion-ld Context Broker and NGSI-ld specifications. The SCEDM model is validated in a system that supports real-time state management across multiple campus domains. The model’s practical operation is validated across several experimental scenarios, including a simulation of a lecture process, classroom occupancy monitoring, and automated notifications to external platforms. In addition, the study compares five international case studies from different contexts. The comparison shows that, despite differences across local settings, similar benefits can be observed in campus operations and learning conditions. The study also recognizes several continuing challenges in the development of smart campuses, including interoperability, long-term scalability, data governance, privacy protection, stakeholder engagement, and financial sustainability. In response to these issues, the authors propose practical design guidelines alongside strategic recommendations for adoption at the institutional level.