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In recent years, our time spent indoors has risen to around 90% and to maintain an occupant's comfort and well-being, Indoor Environmental Quality (IEQ) is monitored. Concerned with inhabitant's satisfaction and health, the adoption of smart solutions for IEQ monitoring and improvement has expanded. The solution this study explores is an occupant-centric approach involving the implementation of an Internet of Things (IoT) IEQ sensing network in a prominent office skyscraper in Hong Kong. Over the course of 15 months, real-time IEQ data were collected from 12 locations within the building. The data were collected at 1-min time intervals and consisted of readings of indoor air temperature, radiant temperature, relative humidity, air velocity, carbon dioxide (CO2), particulate matter (PM10 and PM2.5), horizontal illuminance levels, and sound pressure levels, which served as the basis of the assessment made about the qualities of thermal comfort, indoor air quality (IAQ), aural comfort, and visual comfort. Compared to traditional periodic surveys, this IoT-based sensing network captured instantaneous environmental variations, providing valuable insights into the indoor environment's spatial characterization and temporal dynamics. This smart solution also assisted facility management in terms of identifying sources of discomfort and developing effective mitigation strategies accordingly. This study presents an occupant-centric approach to improve occupant comfort and energy efficiency within office buildings. By customizing the built environment to enhance occupants' well-being, comfort, and productivity, an emphasis is placed on a more personalized and occupant-focused design strategy. This approach integrates technical design with human experience, highlighting the importance of real-time physical and subjective surveys for achieving optimal results. [ABSTRACT FROM AUTHOR]

Current IoT applications in indoor air focus mainly on general monitoring. This study proposed a novel IoT application to evaluate airflow patterns and ventilation performance using tracer gas. The tracer gas is a surrogate for small-size particles and bioaerosols and is used in dispersion and ventilation studies. Prevalent commercial tracer-gas-measuring instruments, although highly accurate, are relatively expensive, have a long sampling cycle, and are limited in the number of sampling points. To enhance the spatial and temporal understanding of tracer gas dispersion under the influence of ventilation, a novel application of an IoT-enabled, wireless R134a sensing network using commercially available small sensors was proposed. The system has a detection range of 5–100 ppm and a sampling cycle of 10 s. Using Wi-Fi communication, the measurement data are transmitted to and stored in a cloud database for remote, real-time analysis. The novel system provides a quick response, detailed spatial and temporal profiles of the tracer gas level, and a comparable air change rate analysis. With multiple units deployed as a wireless sensing network, the system can be applied as an affordable alternative to traditional tracer gas systems to identify the dispersion pathway of the tracer gas and the general airflow direction. [ABSTRACT FROM AUTHOR]