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A context-aware search system for Internet of Things based on hierarchical context model
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  • 作者:Yuanyi Chen ; Jingyu Zhou ; Minyi Guo
  • 关键词:Internet of Things ; Context model ; Searching ; Context recognition
  • 刊名:Telecommunication Systems
  • 出版年:2016
  • 出版时间:May 2016
  • 年:2016
  • 卷:62
  • 期:1
  • 页码:77-91
  • 全文大小:2,421 KB
  • 参考文献:1.Zhang, D., Yang, L. T., & Huang, H. (2011). Searching in Internet of Things: Vision and challenges. In Proceedings of the international symposium on parallel and distributed processing and applications (pp. 201–206).
    2.Kientz, J. A., Patel, S. N., Tyebkhan, A. Z., Gane, B., Wiley, J., & Abowd, G. D. (2006). Where’s my stuff? Design and evaluation of a mobile system for locating lost items for the visually impaired. In Proceedings of the 8th international conference on computers and accessibility (pp. 103–110).
    3.Kruk, S. R. Grzonkowski, S., Gzella, A., & Cygan, M. (2006). DigiMe-Ubiquitous Search and Browsing for Digital Libraries. In Proceedings of the 7th international conference on mobile data management (pp. 84–91).
    4.Frank, C., Bolliger, P., Mattern, F., & Kellerer, W. (2007). Objects calling home: Locating objects using mobile phones. In Proceedings of pervasive (pp. 351–368).
    5.Frank, C., Bolliger, P., Mattern, F., & Kellerer, W. (2008). The sensor internet at work: Locating everyday items using mobile phones. Pervasive and Mobile Computing, 4(3), 421–447.CrossRef
    6.Yap, K. K., Srinivasan, V., Motani, M. (2005). MAX: Human-centric search of the physical world. In Proceedings of the 3rd international conference on embedded networked sensor systems (pp. 166–179).
    7.Komatsuzaki, M., Tsukada, K., Siio, I., Verronen, P., Luimula, M., & Pieska, S. (2011). IteMinder: Finding items in a room using passive RFID tags and an autonomous robot (poster). In Proceedings of the UbiComp’11 (pp. 599–600).
    8.Perera, C., Zaslavsky, A., Christen, P., Compton, M., & Georgakopoulos, D. (2013). Context-aware sensor search, selection and ranking model for Internet of Things middleware. In IEEE 14th International Conference on Mobile Data Management.
    9.Bergamo, P., & Mazzini, G. (2002). Localization in sensor networks with fading and mobility. In IEEE PIMRC (pp. 750–754).
    10.Zhang, D., Zhou, J., Guo, M., & Cao, J. (2011). TASA: Tag-free activity sensing using RFID tag arrays. IEEE Transactions on Parallel and Distributed Systems (TPDS), 22, 558–570.CrossRef
    11.Madden, S. R., Franklin, M. J., Hellerstein, J. M., & Hong, W. (2005). TinyDB: An acquisitional query processing system for sensor networks. ACM Transactions on Database Systems, 30(1), 122–173.CrossRef
    12.Abowd, G. D., Dey, A. K., Brown, P. J., Davies, N., Smith, M., & Steggles, P. (1999). Towards a better understanding of context and context-awareness. Handheld and ubiquitous computing (pp. 304–307). Berlin: Springer.CrossRef
    13.Wang, H., Tan, C. C., & Li, Q. (2010). Snoogle: A search engine for pervasive environments. IEEE Transactions on Parallel and Distributed Systems, 21(8), 1188–1202.CrossRef
    14.Tan, C. C., Sheng, B., Wang, H., & Li, Q. (2010). Microsearch: A search engine for embedded devices used in pervasive computing. ACM Transactions on Embedded Computing Systems, 9(4), 43.
    15.Ejigu, D., Scuturici, M., & Brunie, L. (2007). An ontology-based approach to context modeling and reasoning in pervasive computing. In PerCom’07 (pp. 14–19).
    16.Hung, P. H., Gu, T., Xue, W., Palmes, P. P., Zhu, J., Ng, W. L., et al. (2009). Context-aware middleware for pervasive elderly homecare. IEEE Journal on Selected Areas in Communications, 27(4), 510–524.CrossRef
    17.Heil, A., Knoll, M., & Weis, T. (2007). The Internet of Things—Context-based Device Federations. In Proceedings of the 40th annual Hawaii international conference on system sciences (pp. 58–66).
    18.Ostermaier, B., Roalter, K., Andmer, L. O., Mattern, F., Fahrmair, M., & Kellerer, W. (2010). A real-time search engine for the web of things. In Proceedings of the 2rd international conference on the Internet of Things (pp. 1–8).
    19.Shen, H., Liu, J., Chen, K., Liu, J., & Moyer, S. (2013). SCPS: A social-aware distributed cyber-physical human-centric search engine. IEEE Transactions on Computers, 64(2), 518–532.
    20.Hervás, R., Bravo, J., & Fontecha, J. (2010). A context model based on ontological languages, a proposal for information visualization. Journal of Universal Computer Science, 16(12), 1539–1555.
    21.Holford, M. E., Khurana, E., Cheung, K.-H., & Gerstein, M. (2010). Using semantic web rules to reason on an ontology of pseudogenes. Bioinformatics, 26(12), 71–78.CrossRef
    22.Maekawa, T., Yanagisawa, Y., Sakurai, Y., Kishino, Y., Kamei, K., & Okadome, T. (2012). Context-aware web search in ubiquitous sensor environments. ACM Transactions on Internet Technology, 11(3), 1–23.CrossRef
    23.Qian, H., Mao, Y., Xiang, W., & Wang, Z. (2010). Recognition of human activities using SVM multi-class classifier. Pattern Recognition Letters, 31(2), 100–111.CrossRef
    24.Khan, A. M., Lee, Y. K., Kim, T. S. (2008). Accelerometer signal-based human activity recognition using augmented autoregressive model coefficients and artificial neural nets. In Proceedings of 30th annual IEEE international conference on engineering in medicine and biology society (pp. 5172–5175).
    25.Zhang, D., Zhang, D., Yang, L. T., Gauthier, V., & Xiong, H. (2013). NextCell: Prediction location using cell phone traces. IEEE Transactions on Computers,. doi:10.​1109/​TC.​2013.​223 .
    26.Gu, T., Chen, S., Tao, X., & Lu, J. (2010). An unsupervised approach to activity recognition and segmentation based on object-use fingerprints. Data and Knowledge Engineering, 69(6), 533–544.CrossRef
    27.Guan, D., Yuan, W., Lee, Y., Gavrilov, A., & Lee, S. (2007). Activity recognition based on semi-supervised learning. In Proceedings of 13th IEEE international conference on embedded and real-time computing systems and applications (pp. 469–475).
    28.Stikic, M., Larlus, D., & Schiele, B (2009). Multi-graph based semi-supervised learning for activity recognition. In Proceedings of the 13th IEEE international symposium on wearable computers (pp. 85–92).
    29.Zhang, T., Liu, S., Xu, C., & Lu, H. (2011). Boosted multi-class semi-supervised learning for human action recognition. Pattern Recognition, 44(10–11), 2334–2342.CrossRef
    30.Baum, L. E., Petrie, T., Soules, G., et al. (1970). A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. The Annals of Mathematical Statistics, 41, 164–171.CrossRef
    31.Zhang, D., Yang, L. T., Chen, M., Zhao, S., Guo, M., & Zhang, Y. (2014). A real-time locating system using active RFID for Internet of Things. IEEE Systems Journal,. doi:10.​1109/​JSYST.​2014.​2346625 .
    32.Zhang, D., Guo, M., Zhou, J., Kang, D., & Cao, J. (2010). Context reasoning using extended evidence theory in pervasive computing environments. Future Generation Computer Systems (FGCS), 26, 207–216.CrossRef
    33.Husne, A. R., & Tae-Seong, K. (2010). Activity recognition from single tri-axial accelerometer using wavelet packet decomposition and ANN. In Proceedings of the 2010 international conference on artificial intelligence (pp. 386–390).
  • 作者单位:Yuanyi Chen (1)
    Jingyu Zhou (1)
    Minyi Guo (1)

    1. Embedded & Pervasive Computing Lab, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
  • 刊物类别:Business and Economics
  • 刊物主题:Economics
    Business Information Systems
    Computer Communication Networks
    Artificial Intelligence and Robotics
    Probability Theory and Stochastic Processes
  • 出版者:Springer Netherlands
  • ISSN:1572-9451
文摘
In recent years, numerous sensing devices and wireless networks are immersed into our living environments, creating the Internet of Things (IoT) integrating the cyber and physical objects. Searching for objects in IoT is a challenging problem because the context relationships among IoT objects are various and complex. The traditional web search approaches cannot work well in the IoT search domain because they miss the critical characteristics of the context relationships. In addition, a user’s dynamic and changing context affects the user’s information needs, and an IoT search system should exploit context relationship in IoT for retrieving relevant information suitable for the user’s current context. In this paper, we present a context-aware search system for IoT, which aims to search objects and related information with more suitable results. We construct a hierarchical context model based on ontology to represent the IoT objects and their contextual relationships. Then, searches are executed with consideration of users’ context that is recognized by a context-aware hidden Markov model. Experimental results confirmed that users could obtain more suitable and reasonable search results than with a typical web or map search system.

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