Data-Driven End-to-End Lighting Automation Based on Human Residential Trajectory Analysis

Smart home automation, particularly in lighting, holds the potential to significantly improve comfort, energy efficiency, and security by centralizing control over internet-of-things (loT) devices. This paper introduces a smart lighting automation system that analyzes human movement trajectories using machine learning, deep learning, and reinforcement learning techniques integrated into the Home Assistant platform. In particular, we introduce a transformer-based deep neural network architecture with reward-based tuning as our backbone model. The system predicts the user's next location and adjusts the lighting accordingly based on the anticipated movement trajectories derived from data collected by loT devices distributed throughout a residential area. This enhances both convenience and energy efficiency. We deployed the system in a residential setting and conducted experiments to validate its accuracy.