With the networks of sophisticated sensors and devices, building systems have the potential to serve as the infrastructure that provides essential data for the Internet of Things (IoT)-enabled smart city paradigm. However, current building systems lack intersystem connectivity or exposure to the more extensive networks of IoT devices. In this paper, the authors propose an IoT-enabled data acquisition framework that utilizes low-cost computers, sensors modules, developed software agents, and the existing building Wi-Fi network to establish a central facility database. A system prototype is developed for collecting and integrating facility data, and a case study on a university campus is conducted to demonstrate the proposed framework. The potential use cases enabled by the central facility database, the integration of building information modeling (BIM) standards and building system data protocols, a vision for future smart cities, and the challenges are also discussed. This research concludes that the proposed framework is effective in using IoT devices and networks to establish a cost-effective, platform-neutral, scalable, and portable building data acquisition system for smart building innovations.

Smart buildings are complex systems, yet architecture, engineering, and construction (AEC) professionals often perform their work without considering the human factors of building occupants. Traditionally, the AEC industry has employed a linear design and delivery approach. As buildings become smarter, the AEC industry must adapt. To maximize human well-being and the operational performance of smart buildings, an iterative, human-centred approach must be employed. The omission of human factors in the design and delivery of smart building systems risks misalignment between occupant-user needs and the AEC industry’s perception of occupant-user needs. This research proposes a human-centred approach to smart housing. The study employed a multi-phase, mixed-methods research design. Data were collected from 309 high performance housing units in the United States. Longitudinal energy use data, occupant surveys, and semi-structured interviews are the primary data inputs. Affinity diagramming was leveraged to categorize the qualitative data. The output of the affinity diagramming analysis and energy analysis led to the development of data-driven Personas that communicate smart housing user needs. While these data were gathered in the United States, researchers, practitioners, and policy-makers can leverage the human-centred approach presented in this paper toward the design of other human-centred buildings and infrastructure.