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Thermal comfort refers to the process of determining a pleasant working temperature by considering various environmental, occupational, and personal factors. With the widespread adoption of Internet of Things (IoT) technologies, there is a potential to develop an IoT-based embedded system that directly integrates sensor data with building information to collect the necessary environmental factors for thermal comfort. This paper outlines the development and implementation of an embedded system for remote temperature monitoring, which gathers data such as temperature and humidity using sensors installed within the building and transmits this information to the cloud in real-time via a private Message Queuing Telemetry Transport (MQTT) server. An IoT-based embedded system makes the data collection process more efficient, automated, and integrated, which ultimately leads to a faster and smoother experience in gathering and processing the necessary information to optimize thermal comfort. Following data collection, a machine learning model may be trained using the acquired data to automatically adjust the thermal comfort level in the building, improving overall comfort and energy efficiency.
Nila et al. (Mon,) studied this question.