[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

An improved adaptive dual prediction scheme for reducing data transmission in wireless sensor networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Currently one of the main problem for wireless networks is the medium access control. Hence, the number of data transmissions in wireless sensor networks should be optimized to support more applications and a higher diversity of sensed parameters. In addition, minimizing energy consumption of sensor nodes constitutes one of the main ways to prolong network lifetime. One way to achieve this objective is the exploitation of data prediction technique. This paper presents an innovative idea improving the adaptive dual prediction algorithm without recourse to the data history table to update the model parameters when it drifts. The idea is to exploit immediately the new model parameters performed from the stored ones corresponding to the models used previously during the past prediction phases and eliminated when the threshold imposed by the user exceeded. We carried out simulations using real data of meteorological parameters. We show that our approach achieves up to 99% communication reduction with no significant loss in accuracy.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Samarah, S. (2015). A data predication model for integrating wireless sensor networks and cloud computing. Procedia Computer Science, 52, 1141–1146. https://doi.org/10.1016/j.procs.2015.05.148.

    Article  Google Scholar 

  2. Raza, U., Camerra, A., Murphy, A. L., Palpanas, T., & Picco, G. P. (2015). Practical data prediction for real-world wireless sensor networks. IEEE Transactions on Knowledge and Data Engineering, 27(8), 2231–2244. https://doi.org/10.1109/TKDE.2015.2411594.

    Article  Google Scholar 

  3. Hyndman, R. J., & Athanasopoulos, G. (2017). Forecasting: Principles and practice. http://otexts.com/fpp/2/5/. Accessed 3 Nov 2017.

  4. Wu, M., Tan, L., & Xiong, N. (2016). Data prediction, compression, and recovery in clustered wireless sensor networks for environmental monitoring applications. Information Sciences, 329, 800–818. https://doi.org/10.1016/j.ins.2015.10.004.

    Article  Google Scholar 

  5. Brunelli, D., Dondi, D., Bertacchini, A., Larcher, L., Pavan, P., & Benin, L. (2009). Photovoltaic scavenging systems: Modeling and optimization. Microelectronics Journal, 40(9), 1337–1344. https://doi.org/10.1016/j.mejo.2008.08.013.

    Article  Google Scholar 

  6. Caione, C., Brunelli, D., & Benini, L. (2010). Compressive sensing optimization over ZigBee networks. In 2010 International symposium on industrial embedded system (SIES), pp. 36–44.

  7. Aderohunmu, F. A., Paci, G., Brunelli, D., Deng, J. D., & Benini, L. (2013). Prolonging the lifetime of wireless sensor networks using light-weight forecasting algorithms. In 2013 IEEE eighth international conference on intelligent sensors, sensor networks and information processing, pp. 461–466.

  8. Aderohunmu, F. A., Paci, G., Brunelli, D., Deng, J. D., Benini, L., & Purvis, M. (2013). An application-specific forecasting algorithm for extending WSN lifetime. In 2013 IEEE international conference on distributed computing in sensor systems, pp. 374–381.

  9. Dias, G. M., Bellalta, B., & Oechsner, S. (2016). A survey about prediction-based data reduction in wireless sensor networks. ACM Computing Surveys, 49, 35. https://doi.org/10.1145/2996356.

    Article  Google Scholar 

  10. Jiang, H., Jin, S., & Wang, C. (2011). Prediction or not? An energy-efficient framework for clustering-based data collection in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 22(6), 1064–1071. https://doi.org/10.1109/TPDS.2010.174.

    Article  Google Scholar 

  11. Langendoen, K., & Meier, A. (2010). Analyzing MAC protocols for low data-rate applications. ACM Transactions on Sensor Networks, 7(1), 34. https://doi.org/10.1145/1806895.1806905.

    Article  Google Scholar 

  12. Dias, G. M., Bellalta, B., & Oechsner, S. (2017). The impact of dual prediction schemes on the reduction of the number of transmissions in sensor networks. Computer Communications, 112(1), 58–72. https://doi.org/10.1016/j.comcom.2017.08.002.

    Article  Google Scholar 

  13. Souza, L., Pazzi, R. W., & Nakamura, E. F. (2015). A prediction-based clustering algorithm for tracking targets in quantized areas for wireless sensor networks. Wireless Networks, 21(7), 2263–2278. https://doi.org/10.1007/s11276-015-0914-3.

    Article  Google Scholar 

  14. Nazaktabar, H., Badie, K., & Ahmadabadi, M. N. (2017). RLSP: A signal prediction algorithm for energy conservation in wireless sensor networks. Wireless Networks, 23, 919–933. https://doi.org/10.1007/s11276-016-1200-8.

    Article  Google Scholar 

  15. McCorrie, D. J., Gaura, E., Burnham, K., Poole, N., & Hazelden, R. (2015). Predictive data reduction in wireless sensor networks using selective filtering for engine monitoring. In Wireless sensor and mobile ad-hoc networks: Vehicular and space applications, Springer New York, NY, pp. 129-148. https://doi.org/10.1007/978-1-4939-2468-4_6.

  16. Liu, C., Wu, K., & Tsao, M. (2005). Energy efficient information collection with the ARIMA model in wireless sensor networks. In 2005 IEEE global telecommunications conference, Vol. 5, pp. 2470–2474.

  17. Santini S., & Romer, K. (2006). An adaptive strategy for quality-based data reduction in wireless sensor networks. In Third international conference on networked sensing systems, pp. 29–36.

  18. Tulone D., & Madden, S. (2006). PAQ: Time series forecasting for approximate query answering in sensor networks. In Wireless sensor networks, Vol. 3868, Springer, pp. 21–37. https://doi.org/10.1007/11669463_5.

  19. Clark, B. N., Colbourn, C. J., & Johnson, D. S. (1990). Unit disk graphs. Discrete Mathematics, 86(13), 165–177. https://doi.org/10.1016/0012-365X(90)90358-O.

    Article  MathSciNet  MATH  Google Scholar 

  20. Karp, B., & Kung, H. T. (2000). GPSR: Greedy perimeter stateless routing for wireless networks. In Sixth annual international conference on mobile computing and networking (MobiCom00), pp. 243–254.

  21. Wang, F., & Liu, J. (2011). Networked wireless sensor data collection: Issues, challenges, and approaches. IEEE Communications Surveys & Tutorials, 13(4), 673–687. https://doi.org/10.1109/SURV.2011.060710.00066.

    Article  Google Scholar 

  22. Le Borgne, Y. A., Santini, S., & Bontempi, G. (2007). Adaptive model selection for time series prediction in wireless sensor networks. Signal Process, 87(12), 3010–3020. https://doi.org/10.1016/j.sigpro.2007.05.015.

    Article  MATH  Google Scholar 

  23. Li, G., & Wang, Y. (2013). Automatic ARIMA modeling-based data aggregation scheme in wireless sensor networks. EURASIP Journal on Wireless Communications and Networking, 2013(85), 1–13. https://doi.org/10.1186/1687-1499-2013-85.

    Google Scholar 

  24. Mohanty, S. P. (2015). Nanoelectronic mixed-signal system design. McGraw-Hill. ISBN: 978-0071825719.

  25. Rajaram, M. L. (2015). Comparative analysis and implementation of high data rate wireless sensor network simulation frameworks. Masters Thesis, Department of Engineering Technology, University of North Texas.

  26. Polastre, J., Szewczyk R., & Culler, D. (2005). Telos: Enabling ultra-low power wireless research. In Fourth international symposium on information processing in sensor networks, pp. 364–369.

Download references

Author information

Authors and Affiliations

  1. STIC Laboratory, University of Tlemcen, Tlemcen, Algeria

    Hidaya Liazid & Mohamed Lehsaini

  2. Science Faculty, University of Tlemcen, Tlemcen, Algeria

    Abdelkrim Liazid

  3. LTE Laboratory, ENP-Oran Maurice-Audin, 31000, Oran, Algeria

    Abdelkrim Liazid

Authors
  1. Hidaya Liazid
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed Lehsaini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdelkrim Liazid
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohamed Lehsaini.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liazid, H., Lehsaini, M. & Liazid, A. An improved adaptive dual prediction scheme for reducing data transmission in wireless sensor networks. Wireless Netw 25, 3545–3555 (2019). https://doi.org/10.1007/s11276-019-01950-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-019-01950-7

Keywords

Search

Navigation