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Energy Prediction Model Based on Kernel Partial Least Squares for Energy Harvesting Wireless Sensor Network

  • Xuecai BaoEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 699)

Abstract

In energy harvesting wireless sensor network (EH-WSN), many energy harvesting technologies are developed to sustain the long-term operation of wireless sensor network. However, the prediction of harvested energy plays an important role for energy management. In this paper, we focus on energy prediction for EH-WSN. We first analyze the factors of affecting energy harvesting and the characteristic of the solar array. Then, the kernel partial least squares (KPLS) is proposed as the energy prediction model. According to the difference of energy intake for the days, months, season and year, the four energy prediction models are established. By extensive experimental analysis for real solar data in different areas, the proposed prediction model improves prediction accuracy than existing energy prediction algorithms in EH-WSN.

Keywords

Energy prediction Kernel partial least squares Energy harvesting wireless sensor network 

Notes

Acknowledgement

This research is supported by the National Natural Science Foundation of China (Grant No. 61401189), Natural Science Foundation of Jiangxi, China (Grant No. 20161BAB212036), and Natural Science Fund of Nanchang Institute of technology (Grant No. 2014KJ016).

References

  1. 1.
    Anastasi, G., Conti, M., Francesco, M.D., Passarella, A.: Energy conservation in wireless sensor networks: a survey. Ad Hoc Netw. 7, 537–568 (2009)CrossRefGoogle Scholar
  2. 2.
    Ren, X., Liang, W., Xu, W.: Data collection maximization in renewable sensor networks via time-slot scheduling. IEEE Trans. Comput. 64(7), 1870–1883 (2015)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Kansal, A., Hsu, J., Zahedi, S., Srivastava, M.B.: Power management in energy harvesting sensor networks. ACM Trans. Embed. Comput. Syst. (TECS 2007) 6(4) (2007)Google Scholar
  4. 4.
    Piorno, J., Bergonzini, C., Atienza, D., Rosing, T.: Prediction and management in energy harvested wireless sensor nodes. In: Proceedings of Wireless VITAE 2009, Aalborg, Denmark, pp. 6–10 (2009)Google Scholar
  5. 5.
    Bergonzini, C., Brunelli, D., Benini, L.: Comparison of Energy intake prediction algorithms for systems powered by photovoltaic harvesters. Microelectron. J. 41(11), 766–777 (2010)CrossRefGoogle Scholar
  6. 6.
    Hassan, M., Bermak, A.: Solar harvested energy prediction algorithm for wireless sensors. Qual. Electron. Des. 48(1), 178–181 (2012)Google Scholar
  7. 7.
    Yang, S., Yang, X., Mccann, J.A., Zhang, T.: Distributed networking in autonomic solar powered wireless sensor networks. IEEE J. Sel. Areas Commun. 31(12), 750–761 (2013)CrossRefGoogle Scholar
  8. 8.
    Cammarano, A., Petrioli, C., Spenza, D.: Pro-energy: a novel energy prediction model for solar and wind energy-harvesting wireless sensor networks. In: IEEE International Conference on Mobile Ad-Hoc and Sensor Systems, pp. 75–83 (2012)Google Scholar
  9. 9.
    Rosipal, R., Trejo, L.J.: Kernel partial least squares regression reproducing kernel Hilbert space. J. Mach. Learn. Res. 2, 97–123 (2002)zbMATHGoogle Scholar
  10. 10.

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent ProcessingNanchang Institute of TechnologyNanchangChina

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