Digital Twin-Enhanced Smart Environments: An IoT-AI Framework for Contextual Personalization

The introduction of a Digital Twin enhanced framework, which integrates Internet of Things (IoT) sensing, artificial intelligence (AI), and virtual representations of users and environments, opens up a world of possibilities for delivering personalized context-aware activity recommendations. Real-time environmen-tal and behavioral data are processed using a hybrid wide-and-deep learning model that combines historical feature co-occurrence with deep contextual generalization. The system simulates activity scenarios such as studying in a cafe or exercising in a park, using Digital Twins to predict potential impacts on mood, comfort, and productivity before actions are taken. A continuous feedback loop between physical and digital entities refines predictions over time. An initial evaluation using realworld Ottawa sensor data confirms the model's effectiveness in ranking points of interest based solely on environmental parameters. Unlike conventional recommender systems that rely on static profiles, this framework incorporates real-time contextual variables, such as air quality, noise levels, and UV exposure, to enhance the relevance of recommendations. The convergence of IoT infrastructure, AI modeling, and Digital Twin simulation not only enables scalable, adaptive personalization in smart environments but also paves the way for more informed decisionmaking and improved individual well-being.