Designing Smart Environments: A Paradigm Based on Learning and Prediction

This chapter proposes a learning and prediction based paradigm for designing smart home environments. The foundation of this paradigm lies in information theory as it manages uncertainties in inhabitants’ contexts (e.g., location or mobility, and activities) in daily lives. The underlying idea is to intelligently build compressed dictionaries of context profiles collected from sensor data, efficiently learn from this information, and then predict inhabitant’s future contexts. Successful prediction helps automate device control operations and tasks within the environment as well as to identify anomalies. Thus, the learning and prediction based paradigm optimizes goal functions of smart home environments such as minimizing maintenance cost, manual interactions and energy utilization. After describing some important features of smart environments, this chapter presents the architecture details of our MavHome project. The proposed paradigm is then applied to the inhabitant’s location and activity tracking and prediction, and automated decision making capability. MavHome implementation issues and some practical issues are also discussed.