HomeSGN: A Smarter Home with Novel Rule Mining Enabled by a Scorer-Generator GAN

Most contemporary research in advanced smart homes has been primarily focused on understanding the environment and identifying activities. However, it can never translate these insights into actionable rules that could improve residents' quality of life, much less optimize the entire home environment. Addressing this gap, our paper introduces HomeSGN, an end-to-end trainable Scorer-Generator system founded on the Generative Adversarial Network (GAN) architecture. Specifically tailored for smart home applications, HomeSGN extracts, assesses, and proffers beneficial rules from residents' everyday activities, thereby improving living conditions and optimizing the home environment with adaptable targets. Complemented by pioneering data augmentation and rectification strategies, the system assures model stability, avoids mode collapse, and maintains data integrity throughout GAN training. Integrating HomeSGN into an existing smart home infrastructure establishes a seamless sensor-to-rule pipeline. The effectiveness of HomeSGN is underscored by significant benefits, notably an enhancement of life quality by over 50% in single-user homes and 30% in multi-user scenarios, thus truly embodying the promise of 'smart' in smart homes.