Real estate asset operations typically represent around 80% of investment and management costs, with space management and system monitoring being crucial for user well-being.The AECO industry is moving towards a data-driven model, combining various type of data from various sources. In addition, the inclusion of sensor systems in buildings is leading to a shift towards a distributed semantic approach, requiring a deeper understanding of reality and the ability to link different ontologies from various fields. So, the need to address challenges such as data interoperability, integration of different semantic domains, and improvement of information exchange processes has recognized semantic web technologies as a powerful tool for enhancing the value of BIM models by facilitating data integration and enabling the application of complex queries across multiple data sources. The proposed paper aims to analyze the state of art about IoT and semantic web technologies, and their possible integration in a BIM environment to aid decision-making in building maintenance. It will provide a first ongoing approach for the digitalization of the assets managed by the University of Florence's Building Area. The study shows the two different workflows set up to connect BIM models with sensor data. The first one involved the use of DTH22 environmental sensors integrated through Node-Red to the open source platform Snap4City for data management; the second one involved the installation of LORAWAN sensors within a building and the use of a property platform, Niagara Tridium, for data manipulation and sampling to be convolved within a BMS platform. The work is part of a larger EU Next Generation research project titled, "BIM2DT. BIM-to-Digital Twin: information management to support decision-making in the building life cycle".
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