An Unsupervised Learning Approach for Smart Home Operational Policy Generation

With the rise of the Internet of Things (IoT), smart homes can provide intelligent services to monitor household appliances remotely and automate user tasks. However, a significant amount of human intervention is expected in the deployment and operation of such services, making it inconvenient for human users who are less tech-savvy. Therefore, such systems should be trained to learn user behavior patterns to automatically configure and adapt their actions according to the preferences and daily routines of the occupants with minimal user involvement to enhance user experience. This paper uses an unsupervised learning approach that can be integrated with a generative policy framework. It enables automatic operational policy generation by analyzing the continuous data from sensors and smart devices. In order to generate policies according to user preferences, our process infers users' behavior patterns by looking for the patterns in their daily routine activities. We compared the performance of our proposed learning approach with existing approaches used in generative policy frameworks. Evaluation results show that our proposed approach positively contributes to the automatic policy generation to automate user tasks in smart homes.