Skip to main content
Log in

A new delay jitter smoothing algorithm based on Pareto distribution in Cyber-Physical Systems

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Delay jitter, which can affect the performance of controlled physical system and result in system instability, is an important issue for supporting instantaneity in Cyber-Physical Systems (CPSs) to guarantee continuous, real-time and dependable operation. Thus, it is significant to establish an algorithm to smoothing the delay jitter. In this paper, first, we compare several delay jitter smoothing algorithms, discuss their merits and demerits, and then propose one new algorithm over the existing algorithms, called derivative least square (DLS), which can eliminates the reverse way delay variation spikes. Second, we address the problem of most delay jitter smoothing schemes, namely they used uniformly distributed random numbers to simulate network delay data, which deviates from real network delay. To demonstrate the validity and reliability of DLS, except uniform distribution, Pareto distribution, of which the cumulative distribution function (CDF) is more close to the CDF of real network delay, is applied to conduct the experiment. The experimental results show that compared with the other three existing algorithms, DLS can be more effective and reliable in predicting the play-back delay.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Lee E. CPS foundation. (2010). In Proceedings of the 47th ACM/IEEE Design Automation Conference. Anaheim, USA: IEEE, 737–742.

  2. Wu, F. J., Kao, Y. F., & Tseng, Y. C. (2011). From wireless sensor networks towards cyber physical systems. Pervasive and Mobile Computing, 7(4), 397–413.

    Article  Google Scholar 

  3. Al-Omari H., Wolff F., Papachristou C., et al. (2009). An Improved Algorithm to Smooth Delay Jitter in Cyber-Physical Systems: Scalable Computing and Communications. In Eighth International Conference on Embedded Computing, International Conference On, IEEE, 81–86.

  4. Oklander., Sidi M. (2008). Jitter buffer analysis. In Computer Communications and Networks, ICCCN’08. Proceedings of 17th International Conference on, IEEE, 1–6.

  5. Luck, R., & Ray, A. (1994). Experimental verification of a delay compensation algorithm for integrated communication and control systems. International Journal of Control, 59(6), 1357–1372.

    Article  Google Scholar 

  6. Luck, R., & Ray, A. (1990). An observer-based compensator for distributed delays. Journal of Automatica, 26(5), 903–908.

    Article  Google Scholar 

  7. Liberatore V. (2006). Integrated play-back, sensing, and networked control. In Proceedings of IEEE INFOCOM.

  8. Al-Omari H., Wolff F., Papachristou C., et al. (2009). Avoiding delay jitter in cyber-physical systems using one way delay variations model. In 2009 International Conference on Computational Science and Engineering, IEEE.

  9. Al-Omari, H., Wolff, F., Papachristou, C., et al. (2009). Smoothing delay jitter in networked control systems. Journal of Embedded Computing. doi:10.3233/JEC-2009-0103.

    Google Scholar 

  10. He G., Zhang X. L., Zhang H. M. (2014). A network delay jitter smoothing algorithm in cyber-physical systems. In The 8th FTRA International Conference on Multimedia (MUE).

  11. Leonardi, F., Pinto, A., & Carloni, L. P. (2011). Synthesis of distributed execution platforms for cyber-physical systems with applications to high-performance buildings. Proceedings of the IEEE/ACM International Conference on Cyber-Physical Systems (pp. 215–224). USA: IEEE.

  12. Li, M., Li, Z. J., & Vasilakos, V. A. (2013). A survey on topology control in wireless sensor networks: Taxonomy, comparative study, and open issues. Proceedings of the IEEE, 101(12), 2538–2557.

    Article  Google Scholar 

  13. Youssef, M., Ibrahim, M., Abdelatif, M., et al. (2014). Routing metrics of cognitive radio networks: A survey. IEEE Communications Surveys and Tutorials, 16(1), 92–109.

    Article  Google Scholar 

  14. Sheng, Z. G., Yang, S. S., Yu, Y. F., et al. (2013). A survey on the ietf protocol suite for the internet of things: Standards, challenges, and opportunities. Wireless Communications, IEEE, 20(6), 91–98.

    Article  Google Scholar 

  15. Duan, Q., Yan, Y. H., & Vasilakos, V. A. (2012). A survey on service-oriented network virtualization toward convergence of networking and cloud computing. IEEE Transactions on Network and Service Management, 9(4), 373–392.

    Article  Google Scholar 

  16. Shen, Z. J., Luo, J., & Zimmermann, R. (2011). Peer-to-peer media streaming: Insights and New developments. Proceedings of the IEEE, 99(12), 2089–2109.

    Article  Google Scholar 

  17. Han, K., Luo, J., Liu, Y., et al. (2013). Algorithm design for data communications in duty-cycled wireless sensor networks: A survey. IEEE Communications Magazine, 51(7), 107–113.

  18. Xiong, N. X., Vasilakos, V. A., Yang, T. L., et al. (2009). Comparative analysis of quality of service and memory usage for adaptive failure detectors in healthcare systems. IEEE Journal on Selected Areas in Communications, 27(4), 495–509.

    Article  Google Scholar 

  19. He, D. J., Chen, C., Chan, S., et al. (2012). ReTrust: Attack-resistant and lightweight trust management for medical sensor networks. IEEE Transactions on Information Technology in Biomedicine, 16(4), 623–632.

    Article  Google Scholar 

  20. Zhang, Z. Y., Wang, H. G., Vasilakos, V. A., et al. (2012). ECG-cryptography and authentication in body area networks. IEEE Transactions on Information Technology in Biomedicine, 16(6), 1070–1078.

    Article  Google Scholar 

  21. Zeng, Y. Y., Xiang, K., Li, D., et al. (2013). Directional routing and scheduling for green vehicular delay tolerant networks. Wireless Networks, 19(2), 161–173.

    Article  Google Scholar 

  22. Sengupta, S., Das, S., Nasir, M., et al. (2012). An evolutionary multiobjective sleep-scheduling scheme for differentiated coverage in wireless sensor networks. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 42(6), 1093–1102.

    Article  Google Scholar 

  23. Cheng, H. J., Xiong, N. X., Vasilakos, V. A., et al. (2012). Nodes organization for channel assignment with topology preservation in multi-radio wireless mesh networks. Ad Hoc Networks, 10(5), 760–773.

    Article  Google Scholar 

  24. Song, Y. N., Liu, L., Ma, H. D., et al. (2014). A biology-based algorithm to minimal exposure problem of wireless sensor networks. IEEE Transactions on Network and Service Management, 11(3), 417–430.

    Article  Google Scholar 

  25. Wei, G. Y., Ling, Y., Guo, B. F., et al. (2011). Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman filter. Computer Communications, 34(6), 793–802.

    Article  Google Scholar 

  26. Xiang L., Luo J., Vasilakos V. A. (2011). Compressed data aggregation for energy efficient wireless sensor networks. In 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, 46–54.

  27. Yao, Y. J., Cao, Q., & Vasilakos, V. A. (2013). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for wireless sensor networks. In Mobile Ad-Hoc and Sensor Systems (MASS), 2013 IEEE 10th International Conference (pp. 182–190). Hangzhou: IEEE.

  28. Yao, Y. J., Cao, Q., & Vasilakos, V. A. (2014). EDAL: An energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. doi:10.1109/TNET.2014.2306592.

  29. Li P., Guo S., Yu S., et al. (2012). CodePipe: An opportunistic feeding and routing protocol for reliable multicast with pipelined network coding. INFOCOM. 100–108.

  30. Khan, A. M., Tembine, H., & Vasilakos, V. A. (2012). Game dynamics and cost of learning in heterogeneous 4G networks. IEEE Journal on Selected Areas in Communications, 30(1), 198–213.

    Article  Google Scholar 

  31. Wei, L. F., Zhu, H. J., Dong, X. L., et al. (2014). Security and privacy for storage and computation in cloud computing. Information Sciences, 258, 371–386.

    Article  Google Scholar 

  32. Yilmaz, O. Z. (2001). Seismic data analysis. Tulsa: Society of Exploration Geophysicists.

    Book  Google Scholar 

  33. Zhou X. H., Jiao J. (2011). Application of trend extrapolation method to spectrum analysis of microtremor signal. In 2011 2nd World Congress on Computer Science and Information Engineering (CSIE 2011).

  34. Fu, H. M., & Zhang, S. B. (2003). Theory of derivative extrapolation and prediction. Journal of Mechanical Strength, 25(1), 58–063.

    Google Scholar 

  35. Hernandez, J. A., & Phillips, I. W. (2006). Weibull mixture model to characterise end-to-end internet delay at Coarse Time-Scales. IEEE Communications, 153(2), 295–304.

  36. Zhang W., He J. S. (2007). Modeling End-to-End Delay Using Pareto Distribution. In Second International Conference on Internet Monitoring and Protection (ICIMP).

  37. Zhang, W., & He, J. S. (2007). Statistical modeling and correlation analysis of end-to-end delay in wide area networks. Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 3, 968–973.

    Article  Google Scholar 

Download references

Acknowledgments

This work was financially supported by NFS of China (61363031, 61462007, 61461010), Guilin Sci. and Tech. Development Foundation (20120104-13) and Key Laboratory of Cloud Computing and Complex System Foundation (14101).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ping Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, XL., Liu, P. A new delay jitter smoothing algorithm based on Pareto distribution in Cyber-Physical Systems. Wireless Netw 21, 1913–1923 (2015). https://doi.org/10.1007/s11276-015-0891-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-015-0891-6

Keywords

Navigation