IoT-based personal thermal comfort control for livable environment

Thermal comfort control for indoor environment has become an important issue in smart cities since it is beneficial for people's health and helps to maximize their working productivity and to provide a livable environment. In this article, we present an Internet of things-based personal thermal comfort model with automatic regulation. This model employs some environment sensors such as temperature sensor and humidity sensor to continuously obtain the general environmental measurements. Specially, video cameras are also integrated into the Internet of things network of sensors to capture the individual's activity and clothing condition, which are important factors affecting one's thermal sensation. The individual's condition image can be mapped into different metabolic rates and different clothing insulations by machine learning classification algorithm. Then, all the captured or converted data are fed into a predicted mean vote model to learn the individual's thermal comfort level. In the prediction stage, we introduce the cuckoo search algorithm, which converges rapidly, to solve the air temperature and air velocity with the learnt thermal comfort level. Our experiments demonstrate that the metabolic rates and clothing insulation have great effect on personal thermal comfort, and our model with video capture helps to obtain the variant values regularly, thus maintains the individual's thermal comfort balance in spite of the variations in individual's activity or clothing.