The Internet of Things (IoT) refers to the billions of sensors and connected devices embedded in buildings, factories, hospitals, and cities that continuously collect data about the world around us. Despite the enormous potential of this data to improve how we manage energy, monitor public health, and run our industries, most organizations struggle to make use of it. The reason is that turning raw sensor readings into useful answers requires deep technical expertise. This includes knowing which sensors to consult, which AI models to apply, and how to combine everything correctly. This project addresses this challenge by developing a new approach that allows anyone to simply ask what they want to know (e.g., "which rooms were underused last month and how much energy did they consume?") while the computer system intelligently figures out how to find the answer. By hiding the complexity of sensors, this work has the potential to make the benefits of IoT technology to a much wider range of Americans, including educators, building managers, public health officials, and factory operators, through data management and artificial intelligence. The project also trains the next generation of students through hands-on IoT courses at the university level, high school internships, and a summer camp for students in the Baltimore area. This project advances the foundational science of IoT data management by introducing semantic abstraction as a first-class concept in query processing. Rat