The Sensor Behavior Description and Algorithm in Ambient Intelligence Environment

  • Xiaolong Zeng
  • Zhangqin Huang
  • Yiyuan Ren
  • Chunhua Xiao
  • Lanxin Qiu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 277)

Abstract

Ambient intelligence (AmI) environment is a complicated real-time system with perceptive function and implicit wireless communication ability, which provides humanized and intellectualized service. The previous study of AmI characteristics and intelligent sensor network system architecture makes it feasible to establish a system-level behavioral model of sensor network system in AmI environment. By using POOSL modeling language under the SHESim development environment, a further description class model of system behavior was put forward in this study. Moreover, it proposed a model design sketch of various processing classes and explained the clusters described in the top behavioral model in detail, which lays excellent foundation for establishing performance analysis model in the future intelligent sensor network systems.

Keywords

Ambient intelligence Sensor network system Behavioral analysis Behavioral description class Modeling 

Notes

Acknowledgment

This research was supported by the Beijing Municipal Natural Science Foundation (No. 4122010, 2012.1–2014.12). The authors acknowledge the support of the project of “energy aware model and application of access systems of the Internet of Things.” We wish to thank our reviewers for useful feedback.

References

  1. 1.
    Ducatel GK, Bogdanowicz M (2001) Scenarios for ambient intelligence in 2010. [EB/OL], Feb 2001. http://www.philips.co.kr/Assets/-Downloadablefile/eur19763en-1505.pdf
  2. 2.
    Zeng D, Guo S, Cheng Z, Pham AT (2011) IF-THEN in the internet of things. In: 2011 3rd international conference on awareness science and technology (iCAST), 27–30 Sept 2011, pp 503–507Google Scholar
  3. 3.
    Surie D (2012) Egocentric interaction for ambient intelligence. PhD Thesis, Department of Computing Science, Umeå University, SwedenGoogle Scholar
  4. 4.
    Shen C, Srisathapornphat C, Jaikaeo C (2001) Sensor information networking architecture and applications. IEEE Pers Commun 52–59Google Scholar
  5. 5.
    Paradiso J, Hsiao K, Strickon J, Lifton J, Adler A (2000) Sensor systems for interactive surfaces. IBM Syst J 39(3&4):892–914CrossRefGoogle Scholar
  6. 6.
    Akyildiz I, Su W, Sankarasubramaniam Y, Cayirci E (2002) A survey on sensor networks. IEEE Commun Mag 40(8):102–114CrossRefGoogle Scholar
  7. 7.
    Laibowitz M, Paradiso JA (2005) Parasitic mobility for pervasive sensor networks. In: Third international conference, PERVASIVE 2005, Munich, Germany, May 2005, pp 255–278Google Scholar
  8. 8.
    Schmidt A (2005) Interactive context-aware systems interacting with ambient intelligence. In: Riva G, Vatalaro F, Davide F, Alcañiz M (eds) Ambient intelligence. IOS PressGoogle Scholar
  9. 9.
    Li H, Wang J (2011) Application architecture for ambient intelligence systems based on context ontology modeling. In: 2011 International conference on internet technology and applications, iTAPGoogle Scholar
  10. 10.
    He J, Zhang Y, Hou Y, Huang Z (2009) Study on architecture of ambient intelligence based on context-aware and MAS model. J Beijing Univ Technol 35(2):264–269Google Scholar
  11. 11.
    Chen R (2009) Research on ambient intelligence system architecture. Beijing University of TechnologyGoogle Scholar
  12. 12.
    Chen R, Hou Y, Huang Z, He J (2009) Modeling the ambient intelligence application system: concept, software, data, and network. IEEE Trans Syst Man Cybern Part C Appl Rev 39(3):299–314CrossRefGoogle Scholar
  13. 13.
    Chen R, Hou Y, Huang Z, He J (2009) Data management model for ambient intelligence: Ami-data. In: 2009 WRI international conference on communications and mobile computing, CMC 2009, vol 3, pp 175–180Google Scholar
  14. 14.
    Cheng C, Tse CK (2009) An energy-aware scheduling scheme for wireless sensor networks. IEEE Trans Veh Technol 59(7):3427–3444Google Scholar
  15. 15.
    Malkamaki T, Ovaska SJ (2011) Optimal state estimation for improved power measurements and model verification: theory. In: 2011 International green computing conference and workshops (IGCC), July 2011, pp 1–6Google Scholar
  16. 16.
    Zeng X, Huang Z, Qian S, Ren Y, Xiao C, Wang S (2013) Behavioral analysis and modeling of sensor network system in ambient intelligence environment. In: 4th international conference on intelligent control and information processing, 2013, AcceptedGoogle Scholar
  17. 17.
    Voeten JPM, van der Putten PH, Stevens MPJ (1997) Systematic development of industrial control systems using software/hardware engineering. In: The EUROMICRO conference 97 “New Frontiers of Information Technology”, 1997, pp 21–28Google Scholar
  18. 18.
    Theelen BD, Florescu O, Geilen MCW, Huang J, van der Putten PHA, Voeten JPM (2007). In: Proceedings of the ACM-IEEE international conference on formal methods and models for codesign (MEMOCODE), pp. 139–148. IEEE Computer Society, Los Alamitos (USA). ISBN 1-4244-1050-9Google Scholar
  19. 19.
    van der Putten PHA, Voeten JPM, Stevens MPJ (1995) Object-oriented co-design for hardware/software systems. In: Cavanaugh M (ed) Proceedings of EUROMICRO’95, Los Alamitos, California, USA, pp 718–726Google Scholar
  20. 20.
    Geilen MCW, Voeten JPM (1999) Object-oriented modeling and specification using SHE. In: Proceedings of the 1st international symposium on visual formal methods VFM’99, Eindhoven University of Technology, Eindhoven, The Netherlands, pp 16–24Google Scholar
  21. 21.
    About POOSL (2007). http://www.es.ele.tue.nl/POOSL/
  22. 22.
    Huang Z, Voeten JPM, Theelen BD (2002) Modeling and simulation of a packet switch system using POOSL. Eindhoven University of TechnologyGoogle Scholar
  23. 23.
    Geilen MCW, Voeten JPM, van der Putten PHA, van Bokhoven LJ, Stevens MPJ (2001) Object-oriented modeling and specification using SHE. J Comput Lang 27(3):19–38CrossRefMATHGoogle Scholar
  24. 24.
    Wang S (2009) Behavior modeling and performance analysis on context-aware system in ambient intelligence. MS thesis, Department of Computer Science and Technology, Beijing University of Technology, Beijing, P. R. ChinaGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Xiaolong Zeng
    • 2
  • Zhangqin Huang
    • 1
  • Yiyuan Ren
    • 1
  • Chunhua Xiao
    • 1
  • Lanxin Qiu
    • 1
  1. 1.Embedded Software and System Research InstituteBeijing University of TechnologyBeijingChina
  2. 2.Information and Computer Science Department of Statistics SchoolXi’an University of Finance and EconomicsXi’anChina

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