This Article 
 Bibliographic References 
 Add to: 
Proactive Fuzzy Control and Adaptation Methods for Smart Homes
March/April 2008 (vol. 23 no. 2)
pp. 42-49
Antti-Matti Vainio, Tampere University of Technology
Miika Valtonen, Tampere University of Technology
Jukka Vanhala, Tampere University of Technology
A proactive and ubiquitous computing system in a home environment must operate unobtrusively. This article presents the steps for developing an autonomous home-control system. The system's method uses fuzzy, continuous-time control, online adaptation, and a context-aware intelligent environment. The control and learning methods allow the home to adapt to user routines unobtrusively and smoothly. The adaptation is based on recognizing patterns of human practices, which can change over time. The authors applied these methods to a fuzzy-controlled lighting system in a smart-home laboratory environment. The authors obtained results from both functional and long-term practical tests.

1. D. Tennenhouse, "Proactive Computing," Comm. ACM, vol. 43, no. 5, 2000, pp. 43–50.
2. S. Pirttikangas, Routine Learning: From Reactive to Proactive Environments, doctoral dissertation, Faculty of Technology, Univ. of Oulu, 2004.
3. H.E. Byun and K. Cheverst, "Supporting Proactive 'Intelligent' Behaviour: The Problem of Uncertainty," Workshop User Modeling for Ubiquitous Computing, 9th Int'l Conf. User Modeling 2003 (UM 03), LNCS 2702, Springer, 2003;
4. J. Mäntyjärvi and T. Seppänen, "Adapting Applications in Handheld Devices Using Fuzzy Context Information," Interacting with Computers, vol. 15, no. 4, 2003, pp. 521–538.
5. A.-M. Vainio, M. Valtonen, and J. Vanhala, "Learning and Adaptive Fuzzy Control System for a Smart Home," Developing Ambient Intelligence: Proc. 1st Int'l Conf. Ambient Intelligence Developments, Springer, 2006, pp. 28–47.
6. M. Valtonen, A.-M. Vainio, and J. Vanhala, "Continuous-Time Fuzzy Control and Learning Methods," Proc. 7th Int'l Symp. Communications and Information Technologies (ISCIT07), 2007, pp. 346–351.
7. G. Klir, B. Yuan, and U. St. Clair, Fuzzy Set Theory: Foundations and Applications, Prentice Hall, 1997.
8. E. Mamdani and B. Gaines, Fuzzy Reasoning and Its Applications, Academic Press, 1981.
1. M.C. Mozer, "The Neural Network House: An Environment That Adapts to Its Inhabitants," Proc. AAAI 1998 Spring Symp. Intelligent Environments, AAAI Press, 1998, pp. 110–114.
2. C.D. Kidd et al., "The Aware Home: A Living Laboratory for Ubiquitous Computing Research," Proc. Cooperative Buildings, Integrating Information, Organization, and Architecture, 2nd Int'l Workshop (CoBuild 99), LNCS 1670, Springer, 1999, pp. 191–198.
3. L.-X. Wang and J. Mendel, "Generating Fuzzy Rules by Learning from Examples," IEEE Trans. Systems, Man, and Cybernetics, vol. 22, no. 6, 1992, pp. 1414–1427.
4. F. Hoffmann, "Evolutionary Algorithms for Fuzzy Control System Design," Proc. IEEE, vol. 89, no. 9, 2001, pp. 1318–1333.
5. A. Bonarini, "Comparing Reinforcement Learning Algorithms Applied to Crisp and Fuzzy Learning Classifier Systems," Proc. Genetic and Evolutionary Computation Conf., Morgan Kaufmann, vol. 1, 1999, pp. 52–59.
6. P.Y. Glorennec, "Fuzzy Q-learning and Dynamic Fuzzy Q-learning," Proc. 3rd IEEE Int'l Conf. Fuzzy Systems, IEEE CS Press, 1994, pp. 474–479.
7. F. Doctor, H. Hagras, and V. Callaghan, "A Type-2 Fuzzy Embedded Agent for Ubiquitous Computing Environments," Proc. IEEE Int'l Conf. Fuzzy Systems, vol. 2, IEEE Press, 2004, pp. 1105–1110.

Index Terms:
ambient intelligence, context awareness, adaptive fuzzy control, reinforcement learning
Antti-Matti Vainio, Miika Valtonen, Jukka Vanhala, "Proactive Fuzzy Control and Adaptation Methods for Smart Homes," IEEE Intelligent Systems, vol. 23, no. 2, pp. 42-49, March-April 2008, doi:10.1109/MIS.2008.33
Usage of this product signifies your acceptance of the Terms of Use.