Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

RLSP: a signal prediction algorithm for energy conservation in wireless sensor networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

In many real world applications of wireless sensor networks, it is enough for the sensors to send just an approximation of their observations. In these networks dual prediction scheme (DPS)—including two predictive models one in the sensor side and its copy in the sink side—is widely used. In DPS, the total data transmission through the network is a function of the model’s prediction power and the size of its free parameters. In this paper, a DPS using a reinforcement learning based signal predictor (RLSP) algorithm is proposed. RLSP learns the environment’s signal and builds the predictive model gradually based on its experiences. At the moment the model gets invalid, RLSP only needs to learn and transmit the environmental data of that moment. As a result, the amount of data transmission in the network and consequently energy consumption is very low. The simulation results on 16 benchmarking signals and comparison with time series-based DPSs confirm these properties of RLSP.

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
$34.99 /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 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
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Buonadonna, P., Gay, D., Hellerstein, J. M., Hong, W., & Madden, S. (2005). TASK: Sensor network in a box. In Proceedings of the second European workshop on wireless sensor networks (EWSN’05), Istanbul, Turkey (pp. 133–144). doi:10.1109/ewsn.2005.1462005.

  2. Blagojevic, M., Nabi, M., Geilen, M., Basten, T., Hendriks, T., & Steine, M. (2011). A probabilistic acknowledgment mechanism for wireless sensor networks. In 2011 6th IEEE international conference on networking, architecture and storage (NAS), Dalian, Liaoning, China (pp. 63–72). doi:10.1109/nas.2011.38.

  3. Langendoen, K. (Ed.). (2008). Medium access control in wireless sensor networks. New York, NY: Nova Science Publishers.

    Google Scholar 

  4. Chen, S., Tang, S., Huang, M., & Wang, Y. (2010). Capacity of data collection in arbitrary wireless sensor networks. In INFOCOM, 2010 Proceedings IEEE, San Diego, CA, USA (pp. 1–5). doi:10.1109/infcom.2010.5462214.

  5. Le Borgne, Y. A., Santini, S., & Bontempi, G. (2007). Adaptive model selection for time series prediction in wireless sensor networks. Signal Processing, 87, 3010–3020.

    Article  MATH  Google Scholar 

  6. Wang, L., & Xiao, Y. (2006). A survey of energy-efficient scheduling mechanisms in sensor networks. Mobile Networks and Applications, 11, 723–740.

    Article  Google Scholar 

  7. Anastasi, G., Conti, M., Francesco, M. D., & Passarella, A. (2009). Energy conservation in wireless sensor networks: A survey. Ad Hoc Networks, 7, 537–568.

    Article  Google Scholar 

  8. Luo, C., Wu, F., Sun, J., & Chen, C. W. (2009). Compressive data gathering for large-scale wireless sensor networks. In Proceedings of the 15th annual international conference on mobile computing and networking, Beijing, China. ACM. doi:10.1145/1614320.1614337.

  9. Le Borgne, Y. A., & Bontempi, G. (2005). Round robin cycle for predictions in wireless sensor networks. In 2nd international conference on intelligent sensors, sensor networks and information processing (ISSNIP’05), Melbourne, Australia (pp. 253–258). doi:10.1109/issnip.2005.1595588.

  10. Yao, Y., & Giannakis, G. B. (2005). Energy-efficient scheduling for wireless sensor networks. IEEE Transactions on Communications, 53, 1333–1342.

    Article  Google Scholar 

  11. Min, J.-K., & Chung, C.-W. (2010). EDGES: Efficient data gathering in sensor networks using temporal and spatial correlations. Journal of Systems and Software, 83, 271–282.

    Article  Google Scholar 

  12. Wei, G., Ling, Y., Guo, B., Xiao, B., & Vasilakos, A. V. (2011). Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman filter. Computer Communications, 34, 793–802.

    Article  Google Scholar 

  13. Jain, A., Chang, E. Y., & Wang, Y.-F. (2004). Adaptive stream resource management using Kalman filters. In Proceedings of the 2004 ACM SIGMOD international conference on management of data, New York, NY, USA. ACM. doi:10.1145/1007568.1007573.

  14. Tulone, D., & Madden, S. (2006). PAQ: Time series forecasting for approximate query answering in sensor networks. In 3rd European workshop on wireless sensor networks (EWSN’06), Zurich, Switzerland. Lecture notes in computer science (Vol. 3868, pp. 21–37). doi:10.1007/11669463_5.

  15. Santini, S., & Römer, K. (2006). An adaptive strategy for quality-based data reduction in wireless sensor networks. In Proceedings of the 3rd international conference on networked sensing systems (INSS’06), Chicago, IL, USA.

  16. Chu, D., Deshpande, A., Hellerstein, J. M., & Wei, H. (2006). Approximate data collection in sensor networks using probabilistic models. In ICDE ’06. Proceedings of the 22nd international conference on data engineering, 2006, Atlanta, Georgia (pp. 48–48). doi:10.1109/icde.2006.21.

  17. Le Borgne, Y.-A., & Bontempi, G. (2012). Time series prediction for energy-efficient wireless sensors: Applications to environmental monitoring and video games. In Sensor systems and software, Lisbon, Portugal. Lecture notes of the institute for computer sciences, social informatics and telecommunications engineering (Vol. 102, pp. 63–72). doi:10.1007/978-3-642-32778-0_5.

  18. Soni, S. K., Chand, N., & Singh, D. P. (2012). Reducing the data transmission in WSNs using time series prediction model. In 2012 IEEE international conference on signal processing, computing and control (ISPCC), Waknaghat Solan, HP, India (pp. 1–5). doi:10.1109/ispcc.2012.6224376.

  19. Lazaridis, I., & Mehrotra, S. (2003). Capturing sensor-generated time series with quality guarantees. In 19th international conference on data engineering, 2003. Proceedings. Bangalore, India (pp. 429–440). doi:10.1109/icde.2003.1260811.

  20. Xu, Y., Winter, J., & Lee, W.-C. (2004). Dual prediction-based reporting for object tracking sensor networks. In Proceedings of the 1st International conference on mobile and ubiquitous systems: Networking and services (MobiQuitous’04), Los Alamitos, CA, USA (pp. 154–163): IEEE Computer Society. doi:10.1109/mobiq.2004.1331722.

  21. Intel Research Laboratories Berkeley: Intel Lab Data http://db.csail.mit.edu/labdata/labdata.html. http://berkeley.intel-research.net/labdata/.

  22. Szewczyk, R., Mainwaring, A., Polastre, J., Anderson, J., & Culler, D. (2004). An analysis of a large scale habitat monitoring application. In Proceedings of the 2nd international conference on embedded networked sensor systems, Baltimore, MD, USA. ACM. doi:10.1145/1031495.1031521.

  23. Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction. Cambridge, MA: MIT Press.

    Google Scholar 

  24. National Oceanic Atmospheric Administration’s National Data Buoy Center, Historical data repository: http://www.ndbc.noaa.gov/historical_data.shtml.

  25. Box, G., & Jenkins, G. (1976). Time series analysis: Forecasting and control, Holden-Day series in time series analysis. San Francisco, CA: Holden-Day.

    MATH  Google Scholar 

  26. Makridakis, S., Wheelwright, S., & Hyndman, R. (1998). Forecasting: Methods and applications (3rd ed.). New York, NY: Wiley.

    Google Scholar 

  27. Alexander, S. (1986). Adaptive signal processing: Theory and applications. New York, NY: Springer.

    Book  MATH  Google Scholar 

  28. Polastre, J., Szewczyk, R., & Culler, D. (2005). Telos: Enabling ultra-low power wireless research. In Proceedings of the fourth international symposium on information processing in sensor networks (IPSN 2005), UCLA, Los Angeles, CA, USA (pp. 364–369). doi:10.1109/ipsn.2005.1440950.

Download references

Author information

Authors and Affiliations

  1. Algorithm and Computations Groups, Department of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran

    Hamed Nazaktabar

  2. Knowledge Management and e-Organization Group, IT Research Faculty, Research Institute for ICT, Tehran, Iran

    Kambiz Badie

  3. Control and Intelligent Processing Center of Excellence, School of ECE, College of Engineering, University of Tehran, Tehran, Iran

    Majid Nili Ahmadabadi

Authors
  1. Hamed Nazaktabar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kambiz Badie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Majid Nili Ahmadabadi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Hamed Nazaktabar, Kambiz Badie or Majid Nili Ahmadabadi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-016-1200-8

Keywords

Search

Navigation