Smart Built Environment (SBE): a challenge for Internet of Things (IoT) ecosystems to understand dynamic habitats and their users as complex systems

Accurately modelling building performance remains a major challenge, particularly when aiming to implement predictive control strategies in digital twin systems. The gap between simulated and real building behaviour –often over 30 %– is largely due to the unpredictable nature of human activity and the context-dependent variability of indoor conditions. Beyond energy use, gap between real and controlled comfort is often observed when operation reduces comfort to fixed setpoints. To reduce these gaps, it is key to incorporate in-situ data reflecting how buildings are actually used, occupied, and affected by environmental and operational variables. This paper contributes with an Internet of Things (IoT)-based methodology focused on identifying and analysing key observable fields within Smart Built Environment (SBE). These fields include occupancy patterns, indoor environmental conditions, external climate, architectural configuration, energy flows, system efficiency, and operational cost. This SBE-IoT framework is structured through levels (data, information, knowledge, and cognitive control) and layers (acquisition, ingestion, processing, storage, analysis, understanding and decision-making). From this framework, data are collected and processed through a deployed IoT ecosystem (sensoriZAR) that integrates: real-time acquisition, pattern detection, and multicriteria analysis for supporting Digital Twin (DT) approaches through decision-making and informed-actuation. The proposed SBE-IoT framework was tested in a real-world application to a university building, representative of a smart campus. The experimental results of the cross-relation study have identified comfort oversupply periods (temperatures >21 °C) lasting 30 h/month, causing 7.9 MWh in energy use −45 % of Heating, Ventilation and Air Conditioning (HVAC) demand– and €500/month in costs. This approach proved to be fully operational, low-cost (∼€3000), and rapidly scalable (Return On Investment, ROI <6 months). This SBE-IoT framework enhances digital twin capabilities by providing empirical, context-specific insight to support adaptive, efficient, and replicable decision-making in smart environments. • SBE-IoT framework: Smart Built Environment (SBE) trough Internet of Things (IoT)-driven strategies. • Methodological framework to understand the SBE as a Complex Adaptive System (CAS). • Characterization of SBE-IoT behaviour through seven key observable fields and their patterns. • Obtention of experimental results and cross-relation study through real installations to support Digital Twin (DT) approaches. • Understanding cross-relations among buildings, technologies, and users to overcome the gap between real and controlled comfort in the SBE.