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Augmented/virtual reality (AR/VR) technologies can be deployed in a household environment for applications such as checking the weather or traffic reports, watching a summary of news, or attending classes. Since AR/VR applications are highly delay sensitive, delivering these types of reports in maximum quality could be very challenging. In this paper, we consider that users go through a series of AR/VR experience units that can be delivered at different experience quality levels. In order to maximize the quality of the experience while minimizing the cost of delivering it, we aim to predict the users’ behavior in the home and the experiences they are interested in at specific moments in time. We describe a deep learning based technique to predict the users’ requests from AR/VR devices and optimize the local caching of experience units. We evaluate the performance of the proposed technique on two real-world datasets and compare our results with other baselines. Our results show that predicting users’ requests can improve the quality of experience and decrease the cost of delivery. 

Green homes require informed energy management decisions. For instance, it is preferable that a comfortable internal temperature is achieved through natural, energy-efficient means such as opening doors or lowering shades as opposed to turning on the air conditioning. This requires the control agent to understand the complex system dynamics of the home: will opening the window raise or lower the temperature in this particular situation? Unfortunately, developing mathematical models of a suburban home situated in its natural environment is a significant challenge, while performing real-world experiments is costly, takes a long time and depends on external circumstances beyond the control of the experimenter. In this paper, we describe the architecture of a physical, small scale model of a suburban home and its immediate exterior environment. Specific scenarios can be enacted using Internet of Things (IoT) actuators that control the doors and windows. We use a suite of IoT sensors to collect data during the scenario. We use deep learning-based temporal regression models to make predictions about the impact of specific actions on the temperature and humidity in the home.