Advertisement

Personal and Ubiquitous Computing

, Volume 13, Issue 3, pp 181–196 | Cite as

End-to-end adaptation scheme for ubiquitous remote experimentation

  • Christophe SalzmannEmail author
  • Denis Gillet
  • Philippe Mullhaupt
Original Article

Abstract

Remote experimentation is an effective e-learning paradigm for supporting hands-on education using laboratory equipment at distance. The current trend is to enable remote experimentation in mobile and ubiquitous learning. In such a context, the remote experimentation software should enable effective telemonitoring and teleoperation, no matter the kind of device used to access the equipment. It should also be sufficiently lenient so as to handle the rapidly evolving wireless and mobile communication environment. While the current Internet bandwidth allows remote experimentation to work flawlessly on fixed connections such as LANs, mobile users suffer from both the versatile nature of wireless communications and the limitation of the mobile devices. These conditions impose that the remote experimentation software should integrate adaptation features. For effective ubiquitous remote experimentation, it should ideally be guaranteed that the information representing the state of the remote equipment is rendered (to the end user) at the same pace at which it has been acquired, yet possibly at the cost of a somewhat minimal time delay between the acquisition and rendering phases. In this respect, an end-to-end adaptation scheme is proposed that explicitly handles the inherent variability of the connection and the versatility of the mobile devices considered in ubiquitous remote experimentation. Instead of relying on a stochastic approach, the proposed adaptation scheme relies on a deterministic mass-balance equivalence model. The effectiveness of the proposed adaptation scheme is demonstrated in critical conditions corresponding to remote experimentation carried out using a PDA over a Bluetooth link.

Keywords

End-to-end adaptation Remote experimentation Wireless communication 

References

  1. 1.
    Gillet D, Nguyen AV, Rekik Y (2005) Collaborative web-based experimentation in flexible engineering education. IEEE Trans Educ Spec Issue Web-based Instr 48(4):696–704Google Scholar
  2. 2.
    Ko CC, Chen BM, Chen J, Zhuang Y, Tan KC (2001) Development of a web-based laboratory for control experiments on a coupled tank apparatus. IEEE Trans Educ 44(1)Google Scholar
  3. 3.
    Schmid C (2003) Remote experimentation in control engineering. In: Proceedings of the 11th Mediterranean conference on control and automation MED’03, Rhodos, Paper IV12–01Google Scholar
  4. 4.
    Dabney JB, McCune J, Ghorbel FH (2003) Web-based control of the rice SPENDULAP, Int J Eng Educ 19:478–486Google Scholar
  5. 5.
    Salzmann C, Gillet D, Huguenin P (2000) Introduction to real-time control using LabVIEW™ with an application to distance learning. Int J Eng Educ Spec Issue: LabVIEW Appl Eng Educ 16(3):255–272Google Scholar
  6. 6.
    Tzafestas CS, Palaiologou N, Alifragis M (2006) Virtual and remote robotic laboratory: comparative experimental evaluation. IEEE Trans Educ 49(3):360–369CrossRefGoogle Scholar
  7. 7.
    Callaghan MJ, Harkin J, McGinnity TM, Maguire LP (2003) Adaptive intelligent environment for remote experimentation. In: Proceedings of the IEEE/WIC international conference on web intelligence (WI’03), p. 680Google Scholar
  8. 8.
    Cooper M (2005) Remote laboratories in teaching and learning—issues impinging on widespread adoption in science and engineering education. Int J Online Eng, 1.1. http://www.i-joe.org_/ojs_/viewarticle.php?id=11. Accessed 26 Jun 2005
  9. 9.
    Floyd S (2000) Congestion control principles, RFC 2914, SeptemberGoogle Scholar
  10. 10.
    Various authors, Internet best current practices index, online reference at: http://www.faqs.org_/rfcs_/bcp-index.html
  11. 11.
    Bouch A, Sasse MA, DeMeer H (2000) Of packets and people: a user-centered approach to quality of service. In: Proceedings of the IWQoS2000, pp. 189–197Google Scholar
  12. 12.
    Chalmers D, Sloman M (1999) A survey of quality of service in mobile computing environments. IEEE Communications Surveys, 2nd Quarter, IEEE CSGoogle Scholar
  13. 13.
    Cole RG, Rosenbluth JH (2001) Voice over IP performance monitoring. ACM SIGCOMM Comput Commun Rev 31(2)Google Scholar
  14. 14.
    Aurrecoechea C, Campbell AT, Hauw L (1998) A survey of QoS architectures. ACM/Springer Verlag Multimedia Syst J Spec Issue QoS Arch 6(3):138–151Google Scholar
  15. 15.
    Gracanin D, Zhou Y, DaSilva LA (2004) Quality of service for networked virtual environments. Commun Mag IEEE 42(4):42–48CrossRefGoogle Scholar
  16. 16.
    Anbazhagan M, Nagarajan A, Understanding quality of service for Web services. ftp://www6.software.ibm.com/software/developer/library/ws-quality.pdf. Accessed Jan 2002
  17. 17.
    Zhang Q, Zhu W, Zhang Y-Q (2005) End-to-end QoS for video delivery over wireless internet. Proc IEEE 93(1)Google Scholar
  18. 18.
    Nguyen AV, Rekik Y, Gillet D (2007) Iterative design and evaluation of a web-based experimentation environment. In: Lambropoulos N, Zaphiris P (eds) The book user-centered design of online learning communities. IDEA Group Inc., Hershey, pp 286–313Google Scholar
  19. 19.
    Khamis AM, Rodriguez FJ, Salichs MA (2003) Remote interaction with mobile robots, autonomous robots, vol 15–3. Springer, The Netherlands, pp 267–281Google Scholar
  20. 20.
    MacKenzie IS, Ware C (1993) Lag as a determinant of human performance in interactive systems. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM Press, New York, pp 488–493Google Scholar
  21. 21.
    Borkowski S, Letessier J, Bérard F, Crowley JL (2006) User-centric design of a vision system for interactive applications. In: Proceedings of ICVS ‘06, IEEE international conference on computer vision systemsGoogle Scholar
  22. 22.
    ITU Standard G.114. Audio delay recommendations. http://www.itu.ch
  23. 23.
    Ando N, Lee J, Hashimoto H (1999) A study on influence of time delay in teleoperation. In: Proceeding of the 1999 IEEE/ASME international conference on advanced intelligent mechatronics, Atlanta, USA, pp 317–322Google Scholar
  24. 24.
    Nguyen T, Zakhor A (2002) Distributed video streaming with forward error correction. In: Proceedings of the packet video workshop, Pittsburgh, USAGoogle Scholar
  25. 25.
    Gulliver SR, Ghinea G (2004) Changing frame rate, changing satisfaction? IEEE international conference on multimedia and expo, Taipei, Taiwan, vol 1, pp 177–180Google Scholar
  26. 26.
    Ghinea G, Chen SY (2006) Perceived quality of multimedia educational content: a cognitive style approach. In: Multimedia systems, vol 11–3. Springer, Berlin, pp 271–279Google Scholar
  27. 27.
    Verscheure O, Frossard P, Hamdi M (1999) User-oriented QoS analysis in MPEG-2 video delivery. J Real-Time Imaging Spec Issue Real-Time Digit Video Multimedia Netw 5(5):305–314Google Scholar
  28. 28.
    Floyd S, Fall K (1999) Promoting the use of end-to-end congestion control in the internet. IEEE/ACM Trans Netw 7–4:458–472CrossRefGoogle Scholar
  29. 29.
    Abdelzaher TF, Stankovic JA, Lu C, Zhang R, Lu Y (2003) Feedback performance control in software services. IEEE Control Syst 23(3), 74–90CrossRefGoogle Scholar
  30. 30.
    Bajic IV, Tickoo O, Balan A, Kalyanaraman S, Woods JW (2003) Integrated end-to-end buffer management and congestion control for scalable video communications. In: Proceedings of IEEE international conference on image processing (ICIP), Barcelona, Spain, vol 3, pp III-257–III-260Google Scholar
  31. 31.
    Kim J, Kim Y-G, Song H, Kuo T-Y, Chung YJ, Jay Kuo C-C (2000) TCP-friendly Internet video streaming employing variable frame-rate encoding and interpolation. IEEE Trans Circuits Syst Video Technol 10(7):1164–1177CrossRefGoogle Scholar
  32. 32.
    Gurtov A, Floyd S (2004) Modeling wireless links for transport protocols. ACM CCR 34–2:85–96Google Scholar
  33. 33.
    Legall D (1992) The MPEG video compression algorithm. Image Commun 4:129–140Google Scholar
  34. 34.
    Padhye J, Firoiu V, Towsley D, Kurose J (2000) ModelingTCP reno performance: a simple model and its imperical validation. IEEE/ACM Trans Netw 8(2):133–145CrossRefGoogle Scholar
  35. 35.
    Khalifa I, Trajkovic L (2004) An overview and comparison of analytical TCP models. In: IEEE International Symposium Circuits and Systems, Vancouver, vol V, pp 469–472Google Scholar
  36. 36.
    Yang M, Li XR, Chen H, Rao NSV (2004) Predicting internet end-to-end delay: an overview. In: Proceedings of the 36th IEEE southeastern symposium on systems theory, Atlanta, GA, pp 210–214Google Scholar
  37. 37.
    Jaiswal S, Iannaccone G, Diot C, Kurose J, Towsley D (2004) Inferring TCP connection characteristics through passive measurements. In: Infocom ’04, vol 3, pp 1582–1592Google Scholar
  38. 38.
    Stevens WR (1994) TCP/IP illustrated, vol 1. Addison-Wesley, ReadingzbMATHGoogle Scholar
  39. 39.
    Prasad R, Dovrolis C, Murray M, Claffy K (2003) Bandwidth estimation: metrics, measurement techniques, and tools. Netw IEEE 17–6:27–35CrossRefGoogle Scholar
  40. 40.
    Jain M, Dovrolis C, End-to-end available bandwidth: measurement methodology, dynamics, and relation with TCP throughput. In: Proceedings of ACM SIGCOMM, Pittsburg, PA, USAGoogle Scholar
  41. 41.
    Mahdavi J, Floyd S (1997) TCP-friendly unicast rate-based flow control. Technical note sent to the end2end-interest mailing list. http://www.psc.edu_/networking_/papers_/tcp_friendly.htm. Accessed 8 Jan 1997
  42. 42.
    Widmer J, Denda R, Mauve M (2001) A survey on TCP-friendly congestion control. IEEE Netw 15–3:28–37CrossRefGoogle Scholar
  43. 43.
    Åström KJ, Hagglund T (1995) PID controllers: theory, design and tuning. Instrument Society of America, New YorkGoogle Scholar
  44. 44.
    Gillet D, Fakas G (2001) eMersion: a new paradigm for web-based training in mechanical engineering education. In: International conference on engineering education, Oslo, Norway, vol 8B4, pp 10–14Google Scholar
  45. 45.
    Allman M, Paxson V, Stevens W (1999) TCP congestion control, RFC 2581, proposed standard. ftp://ftp.isi.edu/in-notes/rfc2581.txt
  46. 46.
    Chiu D-M, Jain R (1989) Analysis of the increase and decrease algorithms for congestion avoidance in computer networks. Comput Netw ISDN Syst 17Google Scholar
  47. 47.
    Jacobson V (1988) Congestion avoidance and control. In: ACM SIGCOMM ‘88, pp 314–329Google Scholar
  48. 48.
    eMersion portal. http://emersion.epfl.ch

Copyright information

© Springer-Verlag London Limited 2007

Authors and Affiliations

  • Christophe Salzmann
    • 1
    Email author
  • Denis Gillet
    • 1
  • Philippe Mullhaupt
    • 1
  1. 1.Laboratoire d’automatique, Ecole Polytechnique Fédérale de Lausanne LausanneSwitzerland

Personalised recommendations