A Semantic-Based Approach for Self-Configuring Smart Home Systems Using Feedback-Based Reinforcement Learning

As urban populations grow, smart home technology has become a key enabler for enhancing energy efficiency, comfort, and convenience in residential environments. However, existing smart home implementations often struggle to adapt to complex and dynamic user needs due to issues like device heterogeneity, rigid preset solutions, and limited interoperability. This article presents a novel semantic-based self-configuring framework that captures the functional, operational, state, and characteristic perspectives of smart home devices, facilitating seamless interconnection and interoperability across heterogeneous devices. The proposed framework uses service entity, that is semantically modeled to act as a linker and mediator between applications and devices to enable dynamic scheduling and control based on application requests and user preferences. To enable continuous adaptation and learning, the proposed approach formulates the self-configuration problem as an Markov decision process (MDP), with parameters instantiated using semantic information and rules. A novel feedback-based reinforcement learning approach is then proposed to adjust the reward function and state transition parameters based on user feedback. The performances of the proposed approach against a bench-marked fixed and rule-based mechanisms are demonstrated through simulation.