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Searching the Internet of Things Using Coding Enabled Index Technology

  • Jine Tang
  • Zhangbing ZhouEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11204)

Abstract

With the Internet of Things (IoT) becoming a major component of our daily life, IoT search engines, which can crawl heterogeneous data sources and search in highly dynamic contexts, attract increasing attention from users, industry, and the research community. While considerable effort has been devoted to designing IoT search engines for finding a particular mobile object device, or a list of object devices that fit the query terms description, relatively little attention has been paid to enabling so-called spatial-temporal-keyword query description. This paper identifies an important efficiency problem in existing IoT search engines that simply apply a keyword or spatial-temporal matching to identify object devices that satisfy the query requirement, but that do not simultaneously consider the spatial-temporal-keyword aspect. To shed light on this line of research, we present a novel SMSTK search engine, the core of which is a coding enabled index called STK-tree that seamlessly integrates spatial-temporal-keyword proximity. Further, we propose efficient algorithms for processing range queries. Extensive experiments suggest that SMSTK search engine enables efficient query processing in spatial-temporal-keyword-based object device search.

Keywords

Internet of Things Spatial-temporal-keyword query SMSTK search engine STK-tree Range queries 

Notes

Acknowledgment

The authors gratefully acknowledge the financial support partially from the National Natural Science Foundation of China (No. 61702232, No. 61772479 and No. 61662021), and partially from the higher school research fund from Jiangsu University (No. 1291170040).

References

  1. 1.
    Li, S., Xu, L.D., Zhao, S.: The internet of things: a survey. Inform. Syst. Front. 17, 243–259 (2015)CrossRefGoogle Scholar
  2. 2.
    Pan, J., Jain, R., Paul, S., Vu, T., Saifullah, A., Sha, M.: An Internet of Things framework for smart energy in buildings: designs, prototype, and experiments. IEEE Internet Thing 2, 527–537 (2015)CrossRefGoogle Scholar
  3. 3.
    Park, E., Cho, Y., Han, J., Sang, J.K.: Comprehensive approaches to user acceptance of Internet of Things in a smart home environment. IEEE Internet Thing 4, 2342–2350 (2017)CrossRefGoogle Scholar
  4. 4.
    Zhang, F., Liu, M., Zhou, Z., Shen, W.: An IoT-based online monitoring system for continuous steel casting. IEEE Internet Thing 3, 1355–1363 (2016)CrossRefGoogle Scholar
  5. 5.
    Zhang, P., Liu, Y., Wu, F., Liu, S., Tang, B.: Low-overhead and high-precision prediction model for content-based sensor search in the Internet of Things. IEEE Commun. Lett. 20, 720–723 (2016)CrossRefGoogle Scholar
  6. 6.
    Shemshadi, A., Sheng, Q.Z., Qin, Y.: ThingSeek: a crawler and search engine for the internet of things. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1149–1152. ACM Press, New York (2016)Google Scholar
  7. 7.
    Tan, C.C., Sheng, B., Wang, H., Li, Q.: Microsearch: a search engine for embedded devices used in pervasive computing. ACM Trans. Embed. Comput. 9, 1–43 (2010)CrossRefGoogle Scholar
  8. 8.
    Shah, M., Sardana, A.: Searching in Internet of Things using VCS. In: International Conference on Security of Internet of Things, pp. 63–67. ACM Press, New York (2012)Google Scholar
  9. 9.
    Ma, H., Liu, W.: Progressive search paradigm for Internet of Things. IEEE Multimedia, 1–8 (2010).  https://doi.org/10.1109/mmul.2017.265091429
  10. 10.
    Zhou, Y., De, S., Wei, W., Moessner, K.: Search techniques for the web of things: a taxonomy and survey. IEEE Sens. J. 16, 1–29 (2016)CrossRefGoogle Scholar
  11. 11.
    Aberer, K., Hauswirth, M., Salehi, A.: Infrastructure for data processing in large-scale interconnected sensor networks. In: 2007 International Conference on Mobile Data Management, pp. 198–205. IEEE Press, New York (2007)Google Scholar
  12. 12.
    Wang, H., Tan, C.C., Li, Q.: Snoogle: a search engine for pervasive environments. IEEE Trans. Parallel Distrib. 21, 1188–1202 (2010)CrossRefGoogle Scholar
  13. 13.
    Yap, K.-K., Srinivasan, V., Motani, M.: MAX: human-centric search of the physical world. In: Proceedings of the 3rd International Conference on Embedded Networked Sensor Systems, pp. 166–179. ACM Press, New York (2005)Google Scholar
  14. 14.
    Grosky, W.I., Kansal, A., Nath, S., Liu, J., Zhao, F.: Senseweb: an infrastructure for shared sensing. IEEE Multimedia 14, 8–13 (2007)Google Scholar
  15. 15.
    Ostermaier, B., Römer, K., Mattern, F., Fahrmair, M., Kellerer, W.: A real-time search engine for the web of things. In: IEEE Internet of Things, vol. 9, pp. 1–8 (2010)Google Scholar
  16. 16.
    Ding, Z., Chen, Z., Yang, Q.: IoT-SVKSearch: a real-time multimodal search engine mechanism for the internet of things. Int. J. Commun. Syst. 9, 1–8 (2010)Google Scholar
  17. 17.
    Han, J., Pei, J., Yiwen, Y.: Mining frequent patterns without candidate generation. ACM Sigmod Rec. 29, 1–12 (2010)CrossRefGoogle Scholar
  18. 18.
    Grahne, G., Zhu, J.: Fast algorithms for frequent itemset mining using FP-trees. IEEE Trans. Knowl. Data Eng. 17, 1347–1362 (2005)CrossRefGoogle Scholar
  19. 19.
    Perera, C., Zaslavsky, A., Liu, C.H., Compton, M., Christen, P., Georgakopoulos, D.: Sensor search techniques for sensing as a service architecture for the Internet of Things. IEEE Sens. J. 14, 406–420 (2014)CrossRefGoogle Scholar
  20. 20.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer Science and Communication EngineeringJiangsu UniversityZhenjiangChina
  2. 2.School of Information EngineeringChina University of GeosciencesBeijingChina
  3. 3.Computer Science DepartmentTELECOM SudParisEvryFrance

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