Elements of information theory
Elements of information theory
Proceedings of the 7th annual international conference on Mobile computing and networking
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Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
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IEEE Computer Graphics and Applications
Smart Office: Design of an Intelligent Environment
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CoBuild '99 Proceedings of the Second International Workshop on Cooperative Buildings, Integrating Information, Organization, and Architecture
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PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
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PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
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ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
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IEEE Intelligent Systems
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IEEE Wireless Communications
IEEE Wireless Communications
Modeling Smart Homes for Prediction Algorithms
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
IEEE Network: The Magazine of Global Internetworking
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CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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GPC'11 Proceedings of the 6th international conference on Grid and Pervasive Computing
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Journal of Ambient Intelligence and Smart Environments - A software engineering perspective on smart applications for AmI
Forecasting the behavior of an elderly using wireless sensors data in a smart home
Engineering Applications of Artificial Intelligence
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We propose a learning and prediction based paradigm for designing smart home environments. The foundation of this paradigm lies in information theory as it manages uncertainties of the inhabitants’ contexts (e.g., locations or activities) in daily lives. The idea is to build compressed dictionaries of context-aware data collected from sensors and devices monitoring and/or controlling the smart environment, efficiently learn from these profiles, and finally 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 such goal functions of smart environments as minimizing maintenance cost, manual interactions and energy utilization. After identifying important features of smart environments, we present an overview of our MavHome architecture and apply the proposed paradigm to the inhabitant’s location and activity tracking and prediction, and automated decision-making capability.