Advertisement

Runtime Adaptability of Ambient Intelligence Systems Based on Component-Oriented Approach

  • Muhammed Cagri KayaEmail author
  • Alperen Eroglu
  • Alper Karamanlioglu
  • Ertan Onur
  • Bedir Tekinerdogan
  • Ali H. Dogru
Chapter
Part of the Computer Communications and Networks book series (CCN)

Abstract

Technological improvements of the Internet and connected devices cause increased user expectations. People want to be offered different services in nearly every aspect of their lives. It is a key point that these services can be reached seamlessly and should be dynamically available conforming to the active daily life of today’s people. This can be achieved by having intelligent environments along with smart appliances and applications. The concept of ambient intelligence arises from this need to react with users at runtime and keep providing real-time services under changing conditions. This chapter introduces a component-oriented ontology-based approach to develop runtime adaptable ambient intelligence systems. In this approach, the adaptability mechanism is enabled through a component-oriented method with variability-related capabilities. The outcome supports the find-and-integrate method from the idea formation to the executable system, and thus reducing the need for heavy processes for development. Intelligence is provided through ontology modeling that supports repeatability of the approach in different domains, especially when used in interaction with component variability. In this context, an example problem exploiting the variability in the density of a smart stadium network is used to illustrate the application of the component-driven approach.

Keywords

Ambient intelligence Component-based software development Runtime adaptability Variability modeling Smart networks Smart systems 

References

  1. 1.
    Gámez N, Fuentes L (2011) FamiWare: a family of event-based middleware for ambient intelligence. Pers Ubiquitous Comput 15(4):329–339CrossRefGoogle Scholar
  2. 2.
    Togay C, Dogru AH, Tanik JU (2008) Systematic component-oriented development with axiomatic design. J Syst Softw 81(11):1803–1815CrossRefGoogle Scholar
  3. 3.
    Hansen K, Zang W, Fernandes J, Ingstrup M (2008) Semantic web ontologies for ambient intelligence. In: Proceedings of the 1st international research workshop on the internet of things and services, Sophia-Antipolis, France, pp 1–6Google Scholar
  4. 4.
    Liu Y, Seet BC, Al-Anbuky A (2014) Ambient intelligence context-based cross-layer design in wireless sensor networks. Sensors 14(10):19057–19085CrossRefGoogle Scholar
  5. 5.
    Augusto JC (2006) Ambient intelligence: basic concepts and applications. In: International conference on software and data technologies. Springer, Heidelberg, pp 16–26Google Scholar
  6. 6.
    IST Advisory Group (2001) Scenarios for Ambient Intelligence in 2010, European CommissionGoogle Scholar
  7. 7.
    Ramos C, Augusto JC, Shapiro D (2008) Ambient intelligence—the next step for artificial intelligence. IEEE Intell Syst 23(2):15–18CrossRefGoogle Scholar
  8. 8.
    Augusto JC (2009) Ambient intelligence: opportunities and consequences of its use in smart classrooms. Innov Teach Learn Inf Comput Sci 8(2):53–63Google Scholar
  9. 9.
    Hornos MJ (2017) Application of software engineering techniques to improve the reliability of intelligent environmentsCrossRefGoogle Scholar
  10. 10.
    Sadri F (2011) Ambient intelligence: a survey. ACM Comput Surv (CSUR) 43(4):1–66CrossRefGoogle Scholar
  11. 11.
    Obukata R, Oda T, Barolli L (2016) Design of an ambient intelligence Testbed for improving quality of life. In: Proceedings of the 30th international conference on advanced information networking and applications workshops (WAINA), Crans-Montana, Switzerland. IEEE, pp 714–719Google Scholar
  12. 12.
    Dogru AH, Tanik MM (2003) A process model for component-oriented software engineering. IEEE Softw 2:34–41CrossRefGoogle Scholar
  13. 13.
    Dogru AH (1999) Component oriented software engineering language: COSEML, Technical report TR-99-3, Computer Engineering Department, Middle East Technical University, Ankara, TurkeyGoogle Scholar
  14. 14.
    Kaya MC, Suloglu S, Dogru AH (2014) Variability modeling in component oriented system engineering. In: Proceedings of SDPS the 19th international conference on transformative science and engineering, business and social innovation, Kuching Sarawak Malaysia, 15–19 June 2014Google Scholar
  15. 15.
    Bashari M, Bagheri E, Du W (2017) Dynamic software product line engineering: a reference framework. Int J Softw Eng Knowl Eng 191–234CrossRefGoogle Scholar
  16. 16.
    Ortiz O, García BA, Capilla A, Bosch J, Hinchey M (2012) Runtime variability for dynamic reconfiguration in wireless sensor network product lines. In: Proceedings of the 16th international software product line conference, vol 2. ACM, New York, pp 143–150Google Scholar
  17. 17.
    Gruber TR (1993) A translation approach to portable ontology specifications. Knowl Acquis 5(2):199–220CrossRefGoogle Scholar
  18. 18.
    Ruiz F, Hilera JR (2006) Using ontologies in software engineering and technology. Ontologies for software engineering and software technology. Springer, Heidelberg, pp 49–102CrossRefGoogle Scholar
  19. 19.
    Cetinkaya A, Kaya MC, Dogru AH (2016) Enhancing XCOSEML with connector variability for component oriented development. In: Proceedings of SDPS 21st international conference on emerging trends and technologies in designing healthcare systems, Orlando, FL, USA, 4–6 December 2016Google Scholar
  20. 20.
    Kaya MC, Nikoo MS, Suloglu S, Tekinerdogan B, Dogru AH (2017) Managing heterogeneous communication challenges in the internet of things using connector variability. In: Mahmood Z (ed) Connected environments for the internet of things. Computer Communications and Networks. Springer, ChamGoogle Scholar
  21. 21.
    Basere A, Kostanic I (2017) Spatial sampling requirements for received signal level measurements in cellular networks. In: IEEE 7th annual computing and communication workshop and conference (CCWC), Las Vegas, NV, USA, pp 1–4Google Scholar
  22. 22.
    Locher T, Wattenhofer R, Zollinger A (2005) Received-signal-strength-based logical positioning resilient to signal fluctuation. In: Sixth international conference on software engineering, artificial intelligence, networking and parallel/distributed computing and first ACIS international workshop on self-assembling wireless network, Towson, MD, USA, pp 396–402Google Scholar
  23. 23.
    Eroglu A, Onur E, Turan M (2018) Density-aware outage in clustered ad hoc networks. In: 2018 9th IFIP international conference on new technologies, mobility and security (NTMS). IEEE, pp 1–5Google Scholar
  24. 24.
    Chen L, Zhou S, Xu J (2017) Energy efficient mobile edge computing in dense cellular networks. In: 2017 IEEE international conference on communications (ICC), Paris, France, pp 1–6Google Scholar
  25. 25.
    Apache Jena (2015) A free and open source java framework for building semantic web and linked data applications. https://jena.apache.org/. Accessed 28 Apr 2015
  26. 26.
    Noy NF, Sintek M, Decker S, Crubézy M, Fergerson RW, Musen MA (2001) Creating semantic web contents with protege-2000. IEEE Intell Syst 16(2):60–71CrossRefGoogle Scholar
  27. 27.
    Vallecillos J, Criado J, Padilla N, Iribarne L (2014) A component-based user interface approach for Smart TV. In: 2014 9th international conference on software engineering and applications (ICSOFT-EA), pp 455–463. IEEE, ViennaGoogle Scholar
  28. 28.
    Issarny V, Sacchetti D, Tartanoglu F, Sailhan F, Chibout R, Levy N, Talamona A (2005) Developing ambient intelligence systems: a solution based on web services. Autom Softw Eng 12(1):101–137CrossRefGoogle Scholar
  29. 29.
    Floch J, Hallsteinsen S, Stav E, Eliassen F, Lund K, Gjorven E (2006) Using architecture models for runtime adaptability. IEEE Softw 23(2):62–70CrossRefGoogle Scholar
  30. 30.
    Moisan S, Rigault JP, Acher M, Collet P, Lahire P (2011) Run time adaptation of video-surveillance systems: A software modeling approach. In: International conference on computer vision systems. Springer, Heidelberg, pp. 203–212Google Scholar
  31. 31.
    Homola M, Patkos T, Flouris G, Šefránek J, Šimko A, Frtús J, Baláž M (2015) Resolving conflicts in knowledge for ambient intelligence. Knowl Eng Rev 30(5):455–513CrossRefGoogle Scholar
  32. 32.
    Stavropoulos TG, Vrakas D, Vlachava D, Bassiliades N (2012) Bonsai: a smart building ontology for ambient intelligence. In: Proceedings of the 2nd international conference on web intelligence, mining and semantics, p 30. ACMGoogle Scholar
  33. 33.
    Fan YJ, Yin YH, Da Xu L, Zeng Y, Wu F (2014) IoT-based smart rehabilitation system. IEEE Trans Ind Inform 10(2):1568–1577CrossRefGoogle Scholar
  34. 34.
    Kim J, Park SO (2015) U-health smart system architecture and ontology model. J Supercomput 71(6):2121–2137CrossRefGoogle Scholar
  35. 35.
    Teimourikia M, Fugini M (2017) Ontology development for run-time safety management methodology in smart work environments using ambient knowledge. Futur Gener Comput Syst 68:428–441CrossRefGoogle Scholar
  36. 36.
    Karamanlioglu A, Alpaslan FN (2018) An ontology-based expert system to detect service level agreement violations. In: Proceedings of the 8th international symposium on business modeling and software design, BMSDGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Muhammed Cagri Kaya
    • 1
    Email author
  • Alperen Eroglu
    • 1
  • Alper Karamanlioglu
    • 1
  • Ertan Onur
    • 1
  • Bedir Tekinerdogan
    • 2
  • Ali H. Dogru
    • 1
  1. 1.Department of Computer EngineeringMiddle East Technical UniversityAnkaraTurkey
  2. 2.Information Technology GroupWageningen UniversityWageningenThe Netherlands

Personalised recommendations