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

Comparison of Estimating Missing Values in IoT Time Series Data Using Different Interpolation Algorithms

  • Published:
International Journal of Parallel Programming Aims and scope Submit manuscript

Abstract

When collecting the Internet of Things data using various sensors or other devices, it may be possible to miss several kinds of values of interest. In this paper, we focus on estimating the missing values in IoT time series data using three interpolation algorithms, including (1) Radial Basis Functions, (2) Moving Least Squares (MLS), and (3) Adaptive Inverse Distance Weighted. To evaluate the performance of estimating missing values, we estimate the missing values in eight selected sets of IoT time series data, and compare with those imputed by the standard kNN estimator. Our experiments indicate that in most experiments the estimation based on the Lancaster’s MLS is the best. It is also found that the number of nearest observed values for reference and the distribution of missing values could strongly affect the accuracy of imputation.

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Ahmed, S.H., Rani, S.: A hybrid approach, smart street use case and future aspects for internet of things in smart cities. Future Gener. Comput. Syst. 79, 941–951 (2018). https://doi.org/10.1016/j.future.2017.08.054

    Article  Google Scholar 

  2. Alaa, M., Zaidan, A.A., Zaidan, B.B., Talal, M., Kiah, M.L.M.: A review of smart home applications based on Internet of Things. J. Netw. Comput. Appl. 97, 48–65 (2017). https://doi.org/10.1016/j.jnca.2017.08.017

    Article  Google Scholar 

  3. Beveridge, S.: Least squates estimation of missing values in time series. Commun. Stat.–Theory Methods 21(12), 3479–3496 (1992). https://doi.org/10.1080/03610929208830990

    Article  MathSciNet  Google Scholar 

  4. Bhattacharjee, S., Mitra, P., Ghosh, S.K.: Spatial interpolation to predict missing attributes in GIS using Semantic Kriging. IEEE Trans. Geosci. Remote Sens. 52(8), 4771–4780 (2014). https://doi.org/10.1109/TGRS.2013.2284489

    Article  Google Scholar 

  5. Borgia, E.: The internet of things vision: Key features, applications and open issues. Comput. Commun. 54, 1–31 (2014). https://doi.org/10.1016/j.comcom.2014.09.008

    Article  Google Scholar 

  6. Cuomo, S., Galletti, A., Giunta, G., Marcellino, L.: Reconstruction of implicit curves and surfaces via RBF interpolation. Appl. Numer. Math. 116(SI), 157–171 (2017). https://doi.org/10.1016/j.apnum.2016.10.016

    Article  MathSciNet  MATH  Google Scholar 

  7. Ding, Z., Mei, G., Cuomo, S., Xu, N., Tian, H.: Performance evaluation of gpu-accelerated spatial interpolation using radial basis functions for building explicit surfaces. Int. J. Parallel Program. (2017). https://doi.org/10.1007/s10766-017-0538-6

    Article  Google Scholar 

  8. Haara, A., Maltamo, M., Tokola, T.: The k-nearest-neighbour method for estimating basal-area diameter distribution. Scand. J. Forest Res. 12(2), 200–208 (1997). https://doi.org/10.1080/02827589709355401

    Article  Google Scholar 

  9. Hui, T.K., Sherratt, R.S., Snchez, D.D.: Major requirements for building smart homes in smart cities based on internet of things technologies. Future Gener. Comput. Syst. 76, 358–369 (2017). https://doi.org/10.1016/j.future.2016.10.026

    Article  Google Scholar 

  10. Karkouch, A., Mousannif, H., Moatassime, H.A., Noel, T.: Data quality in internet of things: a state-of-the-art survey. J. Netw. Comput. Appl. 73, 57–81 (2016). https://doi.org/10.1016/j.jnca.2016.08.002

    Article  Google Scholar 

  11. Kouicem, D.E., Bouabdallah, A., Lakhlef, H.: Internet of things security: a top-down survey. Comput. Netw. (2018). https://doi.org/10.1016/j.comnet.2018.03.012

    Article  Google Scholar 

  12. Lu, G.Y., Wong, D.W.: An adaptive inverse-distance weighting spatial interpolation technique. Comput. Geosci. 34(9), 1044–1055 (2008). https://doi.org/10.1016/j.cageo.2007.07.010

    Article  Google Scholar 

  13. Mei, G.: Evaluating the power of GPU acceleration for IDW interpolation algorithm. Sci. World J. (2014). https://doi.org/10.1155/2014/171574

    Article  Google Scholar 

  14. Mei, G., Xu, L., Xu, N.: Accelerating adaptive inverse distance weighting interpolation algorithm on a graphics processing unit. R. Soc. Open Sci. (2017). https://doi.org/10.1098/rsos.170436

    Article  MathSciNet  Google Scholar 

  15. Ouaddah, A., Mousannif, H., Elkalam, A.A., Ouahman, A.A.: Access control in the internet of things: big challenges and new opportunities. Comput. Netw. 112, 237–262 (2017). https://doi.org/10.1016/j.comnet.2016.11.007

    Article  Google Scholar 

  16. Poulos, J., Valle, R.: Missing data imputation for supervised learning. Appl. Artifi. Intell. 32(2), 186–196 (2018). https://doi.org/10.1080/08839514.2018.1448143

    Article  Google Scholar 

  17. Qi, J., Yang, P., Min, G., Amft, O., Dong, F., Xu, L.: Advanced internet of things for personalised healthcare systems: a survey. Pervasive Mobile Comput. 41, 132–149 (2017). https://doi.org/10.1016/j.pmcj.2017.06.018

    Article  Google Scholar 

  18. Ray, P.: A survey on internet of things architectures. J. King Saud Univ. Comput. Inf. Sci. (2016). https://doi.org/10.1016/j.jksuci.2016.10.003

  19. Shepard, D.: A two-dimensional interpolation function for irregularly-spaced data. In: Proceedings of the 1968 23rd ACM national conference, pp. 517–524 (1968)

  20. Shtiliyanova, A., Bellocchi, G., Borras, D., Eza, U., Martin, R., Carrere, P.: Kriging-based approach to predict missing air temperature data. Comput. Electron. Agric. 142(A), 440–449 (2017). https://doi.org/10.1016/j.compag.2017.09.033

    Article  Google Scholar 

  21. Silva, B.N., Khan, M., Han, K.: Towards sustainable smart cities: a review of trends, architectures, components, and open challenges in smart cities. Sustain. Cities Soc. 38, 697–713 (2018). https://doi.org/10.1016/j.scs.2018.01.053

    Article  Google Scholar 

  22. Sovilj, D., Eirola, E., Miche, Y., Bjrk, K.M., Nian, R., Akusok, A., Lendasse, A.: Extreme learning machine for missing data using multiple imputations. Neurocomputing 174, 220–231 (2016). https://doi.org/10.1016/j.neucom.2015.03.108

    Article  Google Scholar 

  23. Stekhoven, D.J., Buehlmann, P.: MissForest-non-parametric missing value imputation for mixed-type data. Bioinformatics 28(1), 112–118 (2012). https://doi.org/10.1093/bioinformatics/btr597

    Article  Google Scholar 

  24. Stojkoska, B.L.R., Trivodaliev, K.V.: A review of internet of things for smart home: Challenges and solutions. J. Clean. Prod. 140, 1454–1464 (2017). https://doi.org/10.1016/j.jclepro.2016.10.006

    Article  Google Scholar 

  25. Tang, F., Ishwaran, H.: Random forest missing data algorithms. Stat. Anal. Data Min. 10(6), 363–377 (2017). https://doi.org/10.1002/sam.11348

    Article  MathSciNet  Google Scholar 

  26. Trappey, A.J.C., Trappey, C.V., Govindarajan, U.H., Chuang, A.C., Sun, J.J.: A review of essential standards and patent landscapes for the Internet of Things: a key enabler for Industry 4.0. Adv. Eng. Inf. 33, 208–229 (2017). https://doi.org/10.1016/j.aei.2016.11.007

    Article  Google Scholar 

  27. Tsai, C.F., Li, M.L., Lin, W.C.: A class center based approach for missing value imputation. Knowl. Based Syst. 151, 124–135 (2018). https://doi.org/10.1016/j.knosys.2018.03.026

    Article  Google Scholar 

  28. Tzounis, A., Katsoulas, N., Bartzanas, T., Kittas, C.: Internet of things in agriculture, recent advances and future challenges. Biosyst. Eng. 164, 31–48 (2017). https://doi.org/10.1016/j.biosystemseng.2017.09.007

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Natural Science Foundation of China (Grant Numbers 11602235 and 41772326), the College Students Innovation and Entrepreneurship Training Program (201811415014), the Fundamental Research Funds for the Central Universities (2652017086).

Author information

Authors and Affiliations

  1. China University of Geosciences (Beijing), 100083, Beijing, China

    Zengyu Ding, Gang Mei, Yixuan Li & Nengxiong Xu

  2. Department of Mathematics and Applications “R. Caccioppoli”, University of Naples Federico II, Naples, Italy

    Salvatore Cuomo

Authors
  1. Zengyu Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gang Mei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Salvatore Cuomo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yixuan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nengxiong Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gang Mei.

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

Ding, Z., Mei, G., Cuomo, S. et al. Comparison of Estimating Missing Values in IoT Time Series Data Using Different Interpolation Algorithms. Int J Parallel Prog 48, 534–548 (2020). https://doi.org/10.1007/s10766-018-0595-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10766-018-0595-5

Keywords

Search

Navigation