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Development of Practical Smart House Scenario Control System

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
PL
Praktyczny system sterowania dla inteligentnego domu
Języki publikacji
EN
Abstrakty
EN
Smart houses have received significant attention in recent years because they are considered to be an ideal living environment. The key point of smart space is that it is self-adjustable to an optimal state through interactions between people and electronic devices. Object detection technology was applied to efficiently calculate the exact number and location of people. The concurrent RFID authentication mechanisms were examined to identify their security threats, and a two-factor RFID security authentication framework is proposed to be integrated into the central controls. The proposed system also combines heterogeneous appliances so that they could adjust themselves correspondingly to various scenarios.
PL
W artykule przedstawiono projekt systemu kontroli inteligentnego domu, opartego na wykorzystaniu czujników, określających ilość i rozmieszczenie ludzi w pomieszczeniach. Wykorzystano także radiowy system zabezpieczeń RFID w celu uwierzytelnienia lokatorów, który w trybie dwu-parametrowym proponowany jest do jednostki sterującej. Zastosowana dodatkowo, niejednorodna struktura urządzenia pozwala mu dopasowywać się do zmieniających się warunków.
Słowa kluczowe
Rocznik
Strony
159--161
Opis fizyczny
Bibliogr. 24 poz., rys., schem.
Twórcy
autor
  • Chinese Culture University
autor
  • Yu Da University
autor
  • Chinese Culture University
Bibliografia
  • [1] Lai, B.-Y., “A Study of Action Groups Discovery for Smart Home,” Master Thesis, Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung, Taiwan, R.O.C. , 2005.
  • [2] Lin, T.-Y., “Design and Implementation of Wireless Lighting Control with Echo Reply for Second Generation Smart Home,” Master Thesis, College of Information and Electrical Engineering, Feng Chia University, Taichung, Taiwan, R.O.C., 2008.
  • [3] Hung, T.-F., “Extracting Spaces from Floor Plans for Smart Home Applications,” Master Thesis, Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung, Taiwan, R.O.C., 2006.
  • [4] Das, S.K., Cook D.J., Battacharya A. et al., The Role of Prediction Algorithms in the MavHome Smart Home Architecture, Wireless Communications, IEEE, 9 (2002) No. 6, 77-84.
  • [5] Heierman, E.O.I., Cook D.J., “Improving Home Automation by Discovering Regularly Occurring Device Usage Patterns,” in Proceedings of the Third IEEE International Conference on Data Mining, 2003, pp. 537-540.
  • [6] Helal, S., Mann W., El-Zabadani H. et al., The Gator Tech Smart House: A Programmable Pervasive Space, Computer, 38 (2005), No. 3, 50-60.
  • [7] Chung, K.H., Oh K.S., Lee C.H. et al., “A user-Centric Approach to De-signing Home Network Devices,” in Proceedings of the Extended abstracts of the 2003 Computer Human Interaction Conference on Human Factors in Computing Systems, Florida, United States, 2003, pp. 648-649.
  • [8] Mäyrä, F., T. Vadén, and I. Koskinen, “Living in metamorphosis: The whys and hows of proactive home design research,” Interactions-Ambient Intelligence: Exploring Our Living Environment, vol. 12(4), pp. 28-31, 2005.
  • [9] Röcker, C., M. D. Janse, N. Portolan, and N. Streitz, "User requirements for intelligent home environments: A scenar iodriven approach and empirical cross-cultural study," in Proceedings of the 2005 joint conference on Smart objects and ambient intelligence, Grenoble: France, 2004, pp. 111-116.
  • [10] Das, S. K. and D. J. Cook, "Designing smart environments: A paradigm based on learning and prediction," in Pattern Recognition and Machine Intelligence, Proceedings. vol. 3776, S. K. Pal, S. Bandyopadhyay, and S. Biswas, Eds., ed Berlin: Springer-Verlag Berlin, 2005, pp. 80-90.
  • [11] Brumitt, B., B. Meyers, J. Krumm, A. Kern, and S. Shafer, "EasyLiving: Technologies for intelligent environments," in Handheld and Ubiquitous Computing, Proceedings. vol. 1927, P. Thomas and H. W. Gellersen, Eds., ed Berlin: Springer- Verlag Berlin, 2000, pp. 12-29.
  • [12] Chen, C.L., Deng Y.Y., , " , Conformation of EPC Class 1 Generation 2 Standards RFID System with Mutual Authentication and Privacy Protection, Engineering Applications of Artificial Intelligence, 22(2009), No. 8, 1284-1291.
  • [13] Jihoon, C., "Strengthening Class1 Gen2 RFID tags," in Mobile Adhoc and Sensor Systems, 2009. MASS '09. IEEE 6th International Conference on, 2009, pp. 818-824.
  • [14] Chien, H.-Y., Chen C.-H., Mutual Authentication Protocol for RFID Conforming to EPC Class 1 Generation 2 Standards, Computer Standards & Interfaces, 29 (2007), No. 2, 254-259.
  • [15] Dang, N. D., J. Park, H. Lee, and K. Kim, "Enhancing Security of EPCglobal Gen-2 RFID Tag against Traceability and Cloning," in Networked RFID Systems and Lightweight Cryptography Raising Barriers to Product Counterfeiting. vol. VIII, P. H. Cole and D. C. Ranasinghe, Eds., ed: Springer, 2008, pp. 269-278.
  • [16] Suleyman, K., and Muhammed Ali B., “Attacks On A Mutual Authentication Scheme Conforming To EPCglobal Class-1 Generation-2 RFID System,” 2009.
  • [17] Burmester, M. and B. De Medeiros, "The security of EPC Gen2 compliant RFID protocols," LNCS, Applied Cryptography and Network Security, vol. 5037, pp. 490-506, 2008.
  • [18] Kumar, P., Lee S.G., and Lee H.J., E-SAP: Efficient-Strong Authentication Protocol for Healthcare Applications Using Wireless Medical Sensor Networks, Sensors, 12 (2012), No. 2, 1625-1647.
  • [19] Malagón-Borja, L., Fuentes O., Object Detection Using Image Reconstruction with PCA, Image and Vision Computing, 27 (2009), 2-9.
  • [20] Bugeau, A., Pérez P., Detection and Segmentation of Moving Objects in Complex Scenes, Comput. Vis. Image Underst., 113 (2009), No. 4, 459-476.
  • [21] Elgammal, A., D. Harwood, and L. D. Davis, "Non-parametric model for background subtraction," in Proceedings of the 6th European Conference on Computer Vision-Part II, Dublin, Ireland, 2000, pp. 751-767.
  • [22] Lee, C.S., Elgammal A., Dynamic Shape Outlier Detection for Human Locomotion, Computer Vision and Image Understanding, 113 (2009), No. 3, 332-344.
  • [23] Otsu, N., A Threshold Selection Method from Gray-level Histograms, IEEE Transactons on Systems, Man and Cybernetics, 9 (1979), No. 1, 62-66.
  • [24] Aronov, B., Bronnimann H., Chang A.Y. et al., Cost-driven Octree Construction Schemes: An Experimental Study, Computational Geometry: Theory and Applications, 31 (2005), No. 1-2, 127-148.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-771e235d-2f40-4950-afa8-0f109d6c106f
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