Development of Practical Smart House Scenario Control System

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. Streszczenie. W artykule przedstawiono projekt systemu kontroli inteligentnego domu, opartego na wykorzystaniu czujnikow, określających ilośc i rozmieszczenie ludzi w pomieszczeniach. Wykorzystano takze radiowy system zabezpieczen RFID w celu uwierzytelnienia lokatorow, ktory w trybie dwu-parametrowym proponowany jest do jednostki sterującej. Zastosowana dodatkowo, niejednorodna struktura urządzenia pozwala mu dopasowywac sie do zmieniających sie warunkow. (Praktyczny system sterowania dla inteligentnego domu).

[1]  Diane J. Cook,et al.  The role of prediction algorithms in the MavHome smart home architecture , 2002, IEEE Wirel. Commun..

[2]  Halim Fathoni,et al.  DEPARTMENT OF COMPUTER SCIENCE AND INFORMATION ENGINEERING , 2008 .

[3]  Jihoon Cho,et al.  Strengthening Class1 Gen2 RFID tags , 2009, 2009 IEEE 6th International Conference on Mobile Adhoc and Sensor Systems.

[4]  Patrick Pérez,et al.  Detection and segmentation of moving objects in complex scenes , 2009, Comput. Vis. Image Underst..

[5]  Pardeep Kumar,et al.  E-SAP: Efficient-Strong Authentication Protocol for Healthcare Applications Using Wireless Medical Sensor Networks , 2012, Sensors.

[6]  Ahmed M. Elgammal,et al.  Dynamic shape outlier detection for human locomotion , 2009, Comput. Vis. Image Underst..

[7]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[8]  Hung-Yu Chien,et al.  Mutual authentication protocol for RFID conforming to EPC Class 1 Generation 2 standards , 2007, Comput. Stand. Interfaces.

[9]  Olac Fuentes,et al.  Object detection using image reconstruction with PCA , 2009, Image Vis. Comput..

[10]  Kwangjo Kim,et al.  Enhancing Security of EPCGlobal Gen-2 RFID against Traceability and Cloning , 2006 .

[11]  Chin-Ling Chen,et al.  Conformation of EPC Class 1 Generation 2 standards RFID system with mutual authentication and privacy protection , 2009, Eng. Appl. Artif. Intell..

[12]  Larry S. Davis,et al.  Non-parametric Model for Background Subtraction , 2000, ECCV.

[13]  Boris Aronov,et al.  Cost-driven octree construction schemes: an experimental study , 2005, Comput. Geom..

[14]  William C. Mann,et al.  The Gator Tech Smart House: a programmable pervasive space , 2005, Computer.

[15]  Norbert A. Streitz,et al.  User requirements for intelligent home environments: a scenario-driven approach and empirical cross-cultural study , 2005, sOc-EUSAI '05.

[16]  Diane J. Cook,et al.  Designing Smart Environments: A Paradigm Based on Learning and Prediction , 2005, PReMI.

[17]  Mike Burmester,et al.  The Security of EPC Gen2 Compliant RFID Protocols , 2008, ACNS.

[18]  Diane J. Cook,et al.  Improving home automation by discovering regularly occurring device usage patterns , 2003, Third IEEE International Conference on Data Mining.

[19]  Barry Brumitt,et al.  EasyLiving: Technologies for Intelligent Environments , 2000, HUC.