Reactive maintenance (RM) is a core service of the operation and maintenance (O&M) phase, the most prolonged and costly within the building lifecycle. RM is characterised by inefficient asset information and communication management, impacting critical FM problems and users’ experience. Building information modelling (BIM) and Internet of things (IoT) has enabled the development of digital twins, moving facilities management (FM) from a reactive approach towards a predictive one. Although previous studies have investigated the application of such technologies to FM, there is a lack of understanding on procedural issues related to its implementation in FM and RM. This research aimed to characterise strategies and decisions involved in prototyping a BIM and IoT-based smart-lighting maintenance system and identify its potential impacts on universities’ maintenance processes. The adopted research strategy and data collection methods involved prototyping, questionnaires, and interviews. The results show a high level of complexity in converging maintenance needs and technological abilities for FM and the importance of procedures and standards at organisational and industry levels. Moreover, it evidenced that the automation of functions and the centralisation of information enabled by BIM and IoT can optimise service provision, generate environmental and efficiency gains, and improve users’ safety and satisfaction.

Robust, fault tolerant, and available systems are fundamental for the adoption of Internet of Things (IoT) in critical domains, such as finance, health, and safety. The IoT infrastructure is often used to collect a large amount of data to meet the business demands of Smart Cities, Industry 4.0, and Smart Home, but there is a opportunity to use these data to intrinsically monitor an IoT system in an autonomous way. A Test Driven Development (TDD) approach for automatic module assessment for ESP32 and ESP8266 IoT development devices based on unsupervised Machine Learning (ML) is proposed to monitor IoT device status. A framework consisting of business drivers, non-functional requirements, engineering view, dynamic system evaluation, and recommendations phases is proposed to be used with the TDD development tool. The proposal is evaluated in academic and smart home study cases with 25 devices, consisting of 15 different firmware versions collected in one week, with a total of over 550,000 IoT status readings. The K-Means algorithm was applied to free memory available, internal temperature, and Wi-Fi level metrics to automatically monitor the IoT devices under development to identify device constraints violation and provide insights for monitoring frequency configuration of different firmware versions. To the best of the authors’ knowledge, it is the first TDD approach for IoT module automatic assessment which uses machine learning based on the real testbed data. The IoT status monitoring and the Python scripts for model training and inference with K-Means algorithm are available under a Creative Commons license.