A Survey of Context Reasoning Methods in Ambient Intelligence
LIU Da-You, LIU Chun-Chen, WANG Sheng-Sheng
College of Computer Science and Technology, Jilin University, Changchun 130012 Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012
Abstract Context reasoning is a key topic in Ambient Intelligence (AmI), which impacts on the intelligent, sensitive, responsive and adaptive ability of AmI systems, and it has gained much attention from researchers in the recent years. The primary research contents, methods and advancements of context reasoning are proposed and analyzed. Exist problems and research directions are also discussed.
[1] Cook D J, Augusto J C, Jakkula V R. Ambient Intelligence: Technologies, Applications, and Opportunities. Pervasive and Mobile Computing, 2009, 5(4): 277-298 [2] Ramos C, Augusto J C, Shapiro D. Ambient Intelligence-The Next Step for Artificial Intelligence. IEEE Intelligent Systems, 2008, 23(2): 15-18 [3] Li Rui, Li Renfa. A Survey of Context-Aware Computing and Its System Infrastructure. Journal of Computer Research and Development, 2007, 44(2): 269-276 (in Chinese) (李 蕊,李仁发.上下文感知计算及系统框架综述.计算机研究与发展, 2007, 44(2): 269-276) [4] Famili A, Shen Weimin, Weber R, et al. Data Preprocessing and Intelligent Data Analysis. International Journal on Intelligent Data Analysis, 1997, 1(1): 3-23 [5] Li Xiaoli. A Brief Review: Acoustic Emission Method for Tool Wear Monitoring during Turning. International Journal of Machine Tools and Manufacture, 2002, 42(2):157-165 [6] Mallat S G. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Trans on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674-693 [7] Li S T, Wang Y N. Multisensor Image Fusion Using Discrete Multiwavelet Transform // Proc of the 3rd International Conference on Visual Computing. Mexico City, Mexico, 2000: 93-103 [8] van Laerhoven K. Teaching Contexts to Applications. Personal and Ubiquitous Computing, 2001, 5(1): 46-49 [9] Hong Xin, Nugent C. Implementing Evidential Activity Recognition in Sensorised Homes. Technology and Health Care, 2011, 19(1): 37-52 [10] Liu W, Hong J, Mctear M F. An Extended Framework for Evidential Reasoning Systems. International Journal of Pattern Recognition and Artificial Intelligence, 1993, 7(3): 441-457 [11] Wan Weijian, Fraser D. Multisource Data Fusion with Multiple Self-Organizing Maps. IEEE Trans on Geoscience and Remote Sensing, 1999, 37(3), 1344-1349 [12] Voegtlin T. Recursive Self-Organizing Maps. Neural Network, 2002, 15(8/9): 979-991 [13] Kanungo T, Mount D M, Netanyahu N S, et al. An Efficient k-Means Clustering Algorithm: Analysis and Implementation. IEEE Trans on Pattern Analysis and Machine Intelligence, 2002, 24(7): 881-892 [14] Ranganathan A, Al-Muhtadi J, Campbell R H. Reasoning about Uncertain Contexts in Pervasive Computing Environments. IEEE Pervasive Computing, 2004, 3(2): 62-70 [15] Anagnostopoulos C B, Ntarladimas Y, Hadjiefthymiades S. Situational Computing: An Innovative Architecture with Imprecise Reasoning. The Journal of Systems and Software, 2007, 80(12): 1993-2014 [16] Ranganathan A, Campbell R H. An Infrastructure for Context-Awareness Based on First Order Logic. Personal and Ubiquitous Computing, 2003, 7(6): 353-364 [17] Gu Tao, Pung H K, Zhang Daqing. Toward an OSGi-Based Infrastructure for Context-Aware Applications. Pervasive Computing, 2004, 3(4): 66-74 [18] Anagnostopoulos C, Hadjiefthymiades S. Advanced Fuzzy Inference Engines in Situation Aware Computing. Fuzzy Sets and Systems, 2010, 161(4): 498-521 [19] Springer T, Turhan A Y. Employing Description Logics in Ambient Intelligence for Modeling and Reasoning about Complex Situations. Journal of Ambient Intelligence and Smart Environments, 2009, 1(3): 235-259 [20] Ko E J, Lee H J, Lee J W. Ontology-Based Context Modeling and Reasoning for U-Health Care. IEICE Trans on Information and Systems, 2007, 90(8): 1262-1270 [21] Wang Xiaohang, Dong Jinsong, Chin C Y, et al. Semantic Space: An Infrastructure for Smart Spaces. Pervasive Computing, 2004, 3(3): 32-39 [22] Chaari T, Ejigu D, Laforest F, et al. A Comprehensive Approach to Model and Use Context for Adapting Applications in Pervasive Environments. The Journal of Systems and Software, 2007, 80(12): 1973-1992 [23] Zimmermann A. Context-Awareness in User Modeling: Requirements Analysis for a Case-Based Reasoning Application // Proc of the 5th International Conference on Case-Based Reasoning. Trondheim, Norway, 2003: 718-732 [24] Kofod-Patersen A, Aamodt A. Contextualised Ambient Intelligence through Case-Based Reasoning // Proc of the 8th European Conference on Case-Based Reasoning. Fethiye, Turkey, 2006: 211-225 [25] Aamodt A. Knowledge-Intensive Case-Based Reasoning in CREEK // Proc of the 7th European Conference on Case-Based Reasoning. Madrid, Spain, 2004: 1-15 [26] Hong Xin, Nugent C, Mulvenna M, et al. Evidential Fusion of Sensor Data for Activity Recognition in Smart Homes. Pervasive and Mobile Computing, 2009, 5(3): 236-252 [27] McClean S, Scotney B. Using Evidence Theory for the Integration of Distributed Databases. International Journal of Intelligent Systems, 1997, 12(10): 763-776 [28] Zhang Daqiang, Guo Minyi, Zhou Jingyu, et al. Context Reasoning Using Extended Evidence Theory in Pervasive Computing Environments. Future Generation Computer Systems, 2010, 26(2): 207-216 [29] Liu Liping, Yager R R. Classic Works of the Dempster-Shafer Theory of Belief Functions: An Introduction // Liu Liping, Yager R R, eds. Studies in Fuzziness and Soft Computing. New York, USA: Springer-Verlag, 2008: 1-34 [30] Zadeh L A. Review of a Mathematical Theory of Evidence. AI Magazine, 1984, 5(3): 81-83 [31] McKeever S, Ye Juan, Coyle L, et al. Activity Recognition Using Temporal Evidence Theory. Journal of Ambient Intelligence and Smart Environments, 2010, 2(3): 253-269 [32] Jirousek R, Preucil S. On the Effective Implementation of the Iterative Proportional Fitting Procedure. Computational Statistics Data Analysis, 1995, 19(2): 177-189 [33] Lauritzen S L. The EM Algorithm for Graphical Association Models with Missing Data. Computational Statistics and Data Analysis, 1995, 19(2): 191-201 [34] Lu C H, Fu Lichen. Robust Location-Aware Activity Recognition Using Wireless Sensor Network in an Attentive Home. IEEE Trans on Automation Science and Engineering, 2009, 6(4): 598-609 [35] Tapia E M, Intille S S, Larson K. Activity Recognition in the Home Using Simple and Ubiquitous Sensors // Proc of the 2nd International Conference on Pervasive Computing. Vienna, Austrlia, 2004: 158-175 [36] Hussermann K, Hubig C, Levi P, et al. Understanding and Designing Situation-Aware Mobile and Ubiquitous Computing Systems // Proc of the International Conference on Mobile, Ubiquitous and Pervasive Computing. Paris, France, 2010: 329-339 [37] Philipose M, Kenneth P, Perkowitz M, et al. Inferring Activities from Interactions with Objects. IEEE Pervasive Computing, 2004, 3(4): 50-57 [38] Mannini A, Sabatini A M. Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers. Sensors, 2010, 10(2): 1154-1175 [39] Haritaoglu I, Harwood D, Davis L S. W4: Real-Time Surveillance of People and Their Activities. IEEE Trans on Pattern Analysis and Machine Intelligence, 2000, 22(8): 809-830 [40] Hamid R, Maddi S, Johnson A, et al. A Novel Sequence Representation for Unsupervised Analysis of Human Activities. Artificial Intelligence, 2009, 173(14): 1221-1244 [41] Kim E, Helal S, Cook D. Human Activity Recognition and Pattern Discovery. IEEE Pervasive Computing, 2010, 9(1): 48-53 [42] Hu D H, Pan S J, Zheng V W, et al. Real World Activity Recognition with Multiple Goals // Proc of the 10th International Conference on Ubiquitous Computing. New York, USA, 2008: 30-39 [43] Favela J, Tentori M, Castro L A. Activity Recognition for Context-Aware Hospital Applications: Issues and Opportunities for the Deployment of Pervasive Networks. Mobile Networks and Applications, 2007, 12(2/3): 155-171 [44] Rivera-illingworth F, Callaghan V, Hagras H. Detection of Normal and Novel Behaviors in Ubiquitous Domestic Environments. Computer Journal, 2007, 53(2): 142-151 [45] Gu Tao, Chen Shaxun, Tao Xiaoping, et al. An Unsupervised Approach to Activity Recognition and Segmentation Based on Object-Use Fingerprints. Data and Knowledge Engineering, 2010, 69(6): 533-544 [46] Dong Guozhu, Li Jinyan. Efficient Mining of Emerging Patterns: Discovering Trends and Differences // Proc of the 5th International Conference on Knowledge Discovery and Data Mining. San Diego, USA, 1999: 43-52 [47] Su Jie, Wu Zhaohui. Context Reasoning Technologies in Ubiquitous Computing Environment //Proc of the International Conference on Embedded and Ubiquitous Computing. Seoul, Korea, 2006: 1027-1036 [48] Himberg J, Flanagan J A, Mntyjrvi J. Toward Context Awareness Using Symbol Clustering Map // Proc of the Workshop for Self-Organizing Maps. Kitakyushu, Japan , 2003: 249-254
2011, Vol. 24 
