Towards IoT-Driven Indoor Wellbeing Optimization and Control Using Multivariate Analysis

Wellness and comfort are key to occupant health, productivity, and satisfaction. The rise of Internet of Things (IoT) is transforming building management by integrating advanced sensors and analytics, enabling intelligent systems that enhance energy efficiency and thermal comfort. This study introduces an innovative framework that combines multivariate analysis (MVA) with IoT technologies to improve indoor environmental wellness and optimize heating, ventilation, and air conditioning (HVAC) efficiency. It utilizes a nonlinear locally weighted regression (LWR) model, enhanced by sequential quadratic programming (SQP), to manage time-dependent variations in environmental factors such as temperature (Temp) and relative humidity (RH). The model was validated using 765 data points collected from three controlled indoor spaces in a university building. Its low computational overhead is compatible with real-time deployment on microcontroller-based platforms, making it well-suited for scalable and adaptive comfort control. Additionally, the framework contributes to more efficient HVAC operation by minimizing unnecessary energy consumption while maintaining occupant satisfaction.