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EnglishIn recent years, the widespread diffusion of pervasive sensing devices and the increasing need for reducing energy consumption have encouraged research in the energy-aware management of smart environments. Following this direction, this paper proposes a hybrid intelligent system which exploits a fog-based architecture to achieve energy efficiency in smart buildings. Our proposal combines reactive intelligence, for quick adaptation to the ever-changing environment, and deliberative intelligence, for performing complex learning and optimization. Such hybrid nature allows our system to be adaptive, by reacting in real time to relevant events occurring in the environment and, at the same time, to constantly improve its performance by learning users' needs. The effectiveness of our approach is validated in the application scenario of a smart home by extensive experiments on real sensor traces. Experimental results show that our system achieves substantial energy savings in the management of a smart environment, whilst satisfying users' needs and preferences.
| Data di pubblicazione: | 2019 |
| Titolo: | A fog-based hybrid intelligent system for energy saving in smart buildings |
| Autori: | |
| Citazione: | De Paola, A., Ferraro, P., Lo Re, G., Morana, M., & Ortolani, M. (2019). A fog-based hybrid intelligent system for energy saving in smart buildings. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING. |
| Rivista: | |
| Digital Object Identifier (DOI): | 10.1007/s12652-019-01375-2 |
| Abstract: | In recent years, the widespread diffusion of pervasive sensing devices and the increasing need for reducing energy consumption have encouraged research in the energy-aware management of smart environments. Following this direction, this paper proposes a hybrid intelligent system which exploits a fog-based architecture to achieve energy efficiency in smart buildings. Our proposal combines reactive intelligence, for quick adaptation to the ever-changing environment, and deliberative intelligence, for performing complex learning and optimization. Such hybrid nature allows our system to be adaptive, by reacting in real time to relevant events occurring in the environment and, at the same time, to constantly improve its performance by learning users' needs. The effectiveness of our approach is validated in the application scenario of a smart home by extensive experiments on real sensor traces. Experimental results show that our system achieves substantial energy savings in the management of a smart environment, whilst satisfying users' needs and preferences. |
| Settore Scientifico Disciplinare: | Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni |
| Appare nelle tipologie: | 1.01 Articolo in rivista |
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