Skip to main content

Experience-Oriented Enhancement of Smartness For Internet of Things

  • Conference paper
  • First Online:
Intelligent Information and Database Systems (ACIIDS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9012))

Included in the following conference series:

Abstract

In this paper, we propose a novel approach, the Experience-Oriented Smart Things that allows experiential knowledge discovery, storage, involving, and sharing for Internet of Things. The main features, architecture, and initial experiments of this approach are introduced. Rather than take all the data produced by Internet of Things, this approach focuses on acquiring only interesting data for its knowledge discovery process. By catching decision events, this approach gathers its own daily operation experience, which is the interesting data, and uses such experience for knowledge discovery. An initial experiment was made at the end of this paper, by applying this approach to a sensors-equipped bicycle, the bicycle is able to learn user’s physical features and recognize its user out of other riders. Customized version of Decisional DNA is used in this approach as the knowledge representation technique. Decisional DNA is a domain-independent, and flexible, and standard experiential knowledge repository solution that allows knowledge to be acquired, reused, evolved and shared easily. The presented conceptual approach demonstrates how knowledge can be discovered through its domain’s experiences and stored as Decisional DNA.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Atzori, L.: Antonio Iera, and Giacomo Morabito.: The internet of things: A survey. Comput. Netw. 54(15), 2787–2805 (2010)

    Article  MATH  Google Scholar 

  2. Ashton, K.: That ‘Internet of Things’ Thing. RFID Journal. http://www.rfidjournal.com/article/print/4986

  3. Tsai, C., et al.: Data Mining for Internet of Things: A Survey. 1-21 (2013)

    Google Scholar 

  4. Kortuem, G., et al.: Smart objects as building blocks for the internet of things. IEEE Internet Comput. 14(1), 44–51 (2010)

    Article  Google Scholar 

  5. Perera, C., et al.: Context aware computing for the internet of things: A survey, 1-41 (2013)

    Google Scholar 

  6. Bandyopadhyay, D., Jaydip, S.: Internet of things: Applications and challenges in technology and standardization. Wireless Pers. Commun. 58(1), 49–69 (2011)

    Article  Google Scholar 

  7. Domingo, M.C.: An overview of the Internet of Things for people with disabilities. Journal of Network and Computer Applications 35(2), 584–596 (2012)

    Article  Google Scholar 

  8. Miorandi, D., et al.: Internet of things: Vision, applications and research challenges. Ad Hoc Netw. 10(7), 1497–1516 (2012)

    Article  Google Scholar 

  9. López, T.S., et al.: Taxonomy, technology and applications of smart objects. Information Systems Frontiers 13(2), 281–300 (2011)

    Article  Google Scholar 

  10. López, T.S., et al.: Adding sense to the Internet of Things. Pers. Ubiquit. Comput. 16(3), 291–308 (2012)

    Article  Google Scholar 

  11. Li, Xu, et al.: Smart community: an internet of things application. Communications Magazine, IEEE 49(11), 68–75 (2011)

    Article  Google Scholar 

  12. Vlacheas, P., et al.: Enabling smart cities through a cognitive management framework for the internet of things. IEEE Communications Magazine 51(6), (2013)

    Google Scholar 

  13. López, T.S., et al.: Adding sense to the Internet of Things. Pers. Ubiquit. Comput. 16(3), 291–308 (2012)

    Article  Google Scholar 

  14. Lee, S.W., Oliver, P., Zeungnam, B.: Applying human learning principles to user-centered IoT systems. Computer 46(2), 46–52 (2013)

    Article  Google Scholar 

  15. Vasseur, J.-P., Adam D.: Interconnecting smart objects with ip: The next internet. Morgan Kaufmann (2010)

    Google Scholar 

  16. The IPSO Alliance. http://www.ipso-alliance.org

  17. Maldonado Sanin, C.A.: Smart Knowledge Management System. PhD Thesis, Faculty of Engineering and Built Environment - School of Mechanical Engineering, University of Newcastle, E. Szczerbicki, Doctor of Philosophy Degree, Newcastle (2007)

    Google Scholar 

  18. Sanin, C., Szczerbicki, E.: Experience-based Knowledge Representation SOEKS. Cybernetics and Systems 40(2), 99–122 (2009)

    Article  MATH  Google Scholar 

  19. Sanin, C., Szczerbicki, E.: An OWL Ontology of Set of Experience Knowledge Structure. Journal of Universal Computer Science 13, 209–223 (2007)

    Google Scholar 

  20. Zhang, H.: Cesar Sanín, and Edward Szczerbicki.: Implementing Fuzzy Logic to Generate User Profile in Decisional DNA Television: The Concept and Initial Case Study. Cybernetics and Systems 44(2–3), 275–283 (2013)

    Article  Google Scholar 

  21. Sanin, C., Mancilla-Amaya, L., Szczerbicki, E., CayfordHowell, P.: Application of a Multi-domain Knowledge Structure: The Decisional DNA. Intel. Sys. For Know. Management, SCI 252, 65–86 (2009)

    Google Scholar 

  22. Sanín, C., Toro, C., Sanchez, E., Mancilla-Amaya, L., Zhang, H., Szczerbicki, E., Crasco, E., Peng, W.: Decisional DNA: A Multi-technology Shareable Knowledge Structure for Decisional Experience. Neurocomputing 88, 42–53 (2012)

    Article  Google Scholar 

  23. Lloyd, J.W.: Logic for Learning: Learning Comprehensible Theories from Structure Data. Springer, Berlin (2003)

    Book  Google Scholar 

  24. The NXP LPCXpresso Board for LPC1769. http://www.nxp.com/demoboard/OM13000.html

  25. Hochbaum, D.S., Shmoys, D.B.: A best possible heuristic for the k-center problem. Mathematics of operations research 10(2), 180–184 (1985)

    Article  MATH  MathSciNet  Google Scholar 

  26. Witten, I. H., Frank, E.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann (2005)

    Google Scholar 

  27. Ali, N., Abu-Elkheir, M.: Data management for the internet of things: green directions. In: Proc. IEEE Globecom Workshops. pp. 386–390 (2012)

    Google Scholar 

  28. Russom, P.: Big data analytics. TDWI Best Practices Report, Fourth Quarter (2011)

    Google Scholar 

  29. Xu, R., Wunsch, D.: Clustering. vol. 10. John Wiley & Sons (2008)

    Google Scholar 

  30. Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: an efficient data clustering method for very large databases. In: ACM SIGMOD Record, vol. 25(2), pp. 103–114. ACM (1996)

    Google Scholar 

  31. Guha, S., Meyerson, A., Mishra, N., Motwani, R., O’Callaghan, L.: Clustering data streams: Theory and practice. IEEE Trans. Knowl. Data Eng. 15(3), 515–528 (2003)

    Article  Google Scholar 

  32. Ng, R.T., Han, J.: CLARANS: A method for clustering objects for spatial data mining. IEEE Trans. Knowl. Data Eng. 14(5), 1003–1016 (2002)

    Article  Google Scholar 

  33. Madden, S.: From databases to big data. IEEE Internet Comput. 16(3), 0004–6 (2012)

    Article  Google Scholar 

  34. Cantoni, V., Lombardi, L., Lombardi, P.: Challenges for data mining in distributed sensor networks. In: IEEE 18th International Conference on Pattern Recognition, ICPR 2006, vol. 1, pp. 1000–1007 (2006)

    Google Scholar 

  35. Baraniuk, R.G.: More is less: signal processing and the data deluge. Science 331(6018), 717–719 (2011)

    Article  Google Scholar 

  36. Ding, C., He, X.: K-means clustering via principal component analysis. In: Proceedings of the twenty-first international conference on Machine learning, p. 29. ACM (2004)

    Google Scholar 

  37. Chiang, M.C., Tsai, C.W., Yang, C.S.: A time-efficient pattern reduction algorithm for k-means clustering. Inf. Sci. 181(4), 716–731 (2011)

    Article  Google Scholar 

  38. Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems 29(7), 1645–1660 (2013)

    Article  Google Scholar 

  39. Aggarwal, C.C., Ashish, N., Sheth, A.: The internet of things: a survey from the data-centric perspective. In: Managing and mining sensor data, pp. 383–428. Springer US (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Haoxi Zhang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Zhang, H., Sanin, C., Szczerbicki, E. (2015). Experience-Oriented Enhancement of Smartness For Internet of Things. In: Nguyen, N., Trawiński, B., Kosala, R. (eds) Intelligent Information and Database Systems. ACIIDS 2015. Lecture Notes in Computer Science(), vol 9012. Springer, Cham. https://doi.org/10.1007/978-3-319-15705-4_49

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-15705-4_49

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15704-7

  • Online ISBN: 978-3-319-15705-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics