Advertisement

A Kernel-Based Approach to Developing Adaptable and Reusable Sensor Retrieval Systems for the Web of Things

  • Nguyen Khoi TranEmail author
  • Quan Z. Sheng
  • M. Ali Babar
  • Lina Yao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10569)

Abstract

In the era of the Web of Things, a vast number of sensors and data streams are accessible to client applications as Web resources. Web Sensor Retrieval systems (WSR) help client applications to access Web-enabled sensors needed for their operation dynamically in an ad-hoc manner. Due to the diversity of sensors and query types, a functional WSR instance must be adaptable to different usage and deployment scenarios to ensure its utility. In this paper, we focus on the systematic reuse of components to enable adaptable WSR. In particular, we propose a modular architecture for WSR and develop a kernel to support the development and composition of WSR modules. We demonstrate our solution with a reference WSR instance deployed on a Raspberry Pi 3. This instance provides five types of queries on eight types of sensors deployed across two sensor platforms. We provide our kernel and reference WSR instance as open-source under MIT license.

Keywords

Web of Things Discovery Search Sensor Architecture 

References

  1. 1.
    BRIDGE: WP02 - High Level Design for Discovery Services (2007). http://www.bridge-project.eu/index.php/workpackage2/en/
  2. 2.
    Christophe, B., Verdot, V., Toubiana, V.: Searching the ‘Web of Things’. In: Proceedings of the 5th IEEE International Conference on Semantic Computing (ICSC), pp. 308–315. IEEE (2011)Google Scholar
  3. 3.
    Ding, Z., Chen, Z., Yang, Q.: IoTSVKSearch: a realtime multimodal search engine mechanism for the internet of things. Int. J. Commun. Syst. 27(6), 871–897 (2014)CrossRefGoogle Scholar
  4. 4.
  5. 5.
    Guinard, D., Trifa, V., Mattern, F., Wilde, E.: From the internet of things to the web of things: resource-oriented architecture and best practices. In: Uckelmann, D., Harrison, M., Michahelles, F. (eds.) Architecting the Internet of Things, vol. 1, 1st edn, pp. 97–129. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-19157-2_5CrossRefGoogle Scholar
  6. 6.
    Mayer, S., Guinard, D.: An extensible discovery service for smart things. In: Proceedings of the Second International Workshop on Web of Things, pp. 1–6. ACM (2011)Google Scholar
  7. 7.
    Mrissa, M., Mdini, L., Jamont, J.P.: Semantic discovery and invocation of functionalities for the web of things. In: Proceedings of the IEEE 23rd International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), pp. 281–286. IEEE (2011)Google Scholar
  8. 8.
    Ngu, A.H.H., Gutierrez, M., Metsis, V., Nepal, S., Sheng, Q.Z.: IoT middleware: a survey on issues and enabling technologies. IEEE Internet Things J. (IoT-J) 4(1), 1–20 (2017)CrossRefGoogle Scholar
  9. 9.
    Open-Geospatial-Consortium: OGC SensorThings API Part I: Sensing - OGC Implementation Standard (2016). http://docs.opengeospatial.org/is/15-078r6/15-078r6.html
  10. 10.
    Ostermaier, B., Romer, K., Mattern, F., Fahrmair, M., Kellerer, W.: A real-time search engine for the web of things. In: Proceedings of the 1st International Conference on the Internet of Things (IOT), pp. 1–8. IEEE (2010)Google Scholar
  11. 11.
    Perera, C., Zaslavsky, A., Christen, P., Compton, M., Georgakopoulos, D.: Context-aware sensor search, selection and ranking model for internet of things middleware. In: Proceedings of the IEEE 14th International Conference on Mobile Data Management (MDM), vol. 1, pp. 314–322. IEEE (2013)Google Scholar
  12. 12.
    Rezafard: Extensible Supply-chain Discovery Service Problem Statement (2008). http://tools.ietf.org/html/draft-rezafard-esds-problem-statement-03
  13. 13.
    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 (2016)Google Scholar
  14. 14.
    Sheng, Q.Z., Qin, Y., Yao, L., Benatallah, B.: Managing the Web of Things: Linking the Real World to the Web, vol. 1. Morgan Kaufmann, Burlington (2017)Google Scholar
  15. 15.
    Tan, C.C., Sheng, B., Wang, H., Li, Q.: Microsearch: when search engines meet small devices. In: Indulska, J., Patterson, D.J., Rodden, T., Ott, M. (eds.) Pervasive 2008. LNCS, vol. 5013, pp. 93–110. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-79576-6_6CrossRefGoogle Scholar
  16. 16.
    Trifa, V., Guinard, D., Carrera, D.: Web thing model (2015). http://www.w3.org/Submission/wot-model/
  17. 17.
    Truong, C., Romer, K., Chen, K.: Fuzzy-based sensor search in the web of things. In: Proceedings of the 3rd International Conference on the Internet of Things (IOT), pp. 127–134. IEEE (2012)Google Scholar
  18. 18.
    Wang, H., Tan, C.C., Li, Q.: Snoogle: a search engine for pervasive environments. IEEE Trans. Parallel Distrib. Syst. 21(8), 1188–1202 (2010)CrossRefGoogle Scholar
  19. 19.
    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 (2005)Google Scholar
  20. 20.
    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(4), 720–723 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Nguyen Khoi Tran
    • 1
    Email author
  • Quan Z. Sheng
    • 2
  • M. Ali Babar
    • 1
  • Lina Yao
    • 3
  1. 1.The University of AdelaideAdelaideAustralia
  2. 2.Macquarie UniversitySydneyAustralia
  3. 3.The University of New South WalesSydneyAustralia

Personalised recommendations