CFP last date
22 July 2024
Call for Paper
August Edition
IJCA solicits high quality original research papers for the upcoming August edition of the journal. The last date of research paper submission is 22 July 2024

Submit your paper
Know more
Reseach Article

Efficient Spatial Data Recovery Scheme with Error Refinement for Cyber-physical Systems

by Naushin Nower, Yasuo Tan, Yuto Lim
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 26
Year of Publication: 2018
Authors: Naushin Nower, Yasuo Tan, Yuto Lim

Naushin Nower, Yasuo Tan, Yuto Lim . Efficient Spatial Data Recovery Scheme with Error Refinement for Cyber-physical Systems. International Journal of Computer Applications. 179, 26 ( Mar 2018), 34-40. DOI=10.5120/ijca2018916558

@article{ 10.5120/ijca2018916558,
author = { Naushin Nower, Yasuo Tan, Yuto Lim },
title = { Efficient Spatial Data Recovery Scheme with Error Refinement for Cyber-physical Systems },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2018 },
volume = { 179 },
number = { 26 },
month = { Mar },
year = { 2018 },
issn = { 0975-8887 },
pages = { 34-40 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2018916558 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:56:35.748642+05:30
%A Naushin Nower
%A Yasuo Tan
%A Yuto Lim
%T Efficient Spatial Data Recovery Scheme with Error Refinement for Cyber-physical Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 26
%P 34-40
%D 2018
%I Foundation of Computer Science (FCS), NY, USA

Feedback data loss can severely degrade the overall system performance and as well as it can affect the control and computation of the Cyber-physical Systems (CPS). CPS hold enormous potential for a wide range of emerging and time critical applications including different traffic patterns. Therefore, incomplete feedback makes a great challenge in any uncertain condition to maintain the real-time control of the CPS. In this paper, we proposed a data recovery called Efficient Spatial Data Recovery with an error refinement (ESDR/ER) procedure for CPS to minimize the error estimation and maximize the accuracy of the scheme. In this scheme, we present an algorithm with Pearson Correlation Coefficient (PCC) to efficiently solve the missing data for both deterministic and stochastic traffic patterns. We also present an error refinement procedure to refine the error thus to maintain high accuracy. Numerical results reveal that the proposed ESDR/ER outperforms both WP and STI algorithms regardless of the increment percentage of missing data in terms of the root mean square error, mean absolute error and integral of absolute error.

  1. A.L. Edward, “Cyber physical systems: Design challenges,” IEEE Symp. on Object Oriented Real-Time Distributed Computing, pp.363–369, 2008.
  2. A.L. Edward, “CPS foundations,” ACM/IEEE Design Automation Conf. (DAC), pp.737–742, 2010.
  3. F.J. Wu, Y.F. Kao, and Y.C. Tseng, “From wireless sensor networks towards cyber physical systems,” J. Pervasive and Mobile Comp., vol.7, no.4, pp.397–413, 2011.
  4. F. Martincic and L. Schwiebert, Introduction to wireless sensor networking, Handbook of Sensor Networks—Algorithms and Architectures. John Wiley & Sons; New York, USA, 2005.
  5. A.L. Edward, “Towards a science of cyber-physical system design,” ACM/IEEE Conf. on Cyber-physical System, pp.99–108, April 2011.
  6. N.Nower, T.Yasuo, A.O. Lim, “Efficient Spatial Data Recovery Scheme for Cyber-physical System,” IEEE Int. Conf. on Cyber-Physical Systems, Networks and Applications, pp.72-77,2013.
  7. R.J.A. Little and D.B. Rubin, Statistical Analysis with Missing Data, 2nd Ed., Wiley-Interscience, New York, 2002.
  8. D.C. Howell. University of Vermont. (2009). Treatment of missing data [Online]. Available:
  9. J.G. Ibrahim, H. Zhu and N. Tang, “Model selection criteria for missing-data problems using the EM algorithm,” J. American Statistical Association. pp.1648–1658, 2008.
  10. H.Y. Chen, H. Xie and Y. Qian, “Multiple imputation for missing values through conditional semi parametric odds ratio models,” J. Biometrics vol.67, no.3, pp.799–809, 2011.
  11. J.M.I. Molina, P.J. Garcia-Laencina, E. Alba, N. Ribelles, M. Martin and L. Franco, “Missing data imputation using statistical and machine learning methods in a real breast cancer problem,” J. Artificial Intelligence in Machine, Elsevier Science Publishers, vol.50, no.2, pp.105–115, 2010.
  12. C.M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), Springer Science Business Media, 2007.
  13. T. Kohonen, Self-organizing Maps, Springer Series in Information Sciences, Springer-Verlag; 3rd Ed., 2001.
  14. C.C. Huang and H.M. Lee, "A grey-based nearest neighbor approach for missing attribute value prediction, "J. Applied Intelligence, vol.20, no.3, pp.239–252, 2004.
  15. W. Bajwa, “Compressive wireless sensing,” ACM Conf. on Inf. Processing in Sensor Networks, pp.134–142, 2006.
  16. D. Guo, X. Qu, L. Huang and Y. Yao.,“Sparsity-based spatial interpolation in wireless sensor networks,” J. Sensors, vol.11, no.3, pp.2385–2407, 2011.
  17. Y.Y. Li and L.E Parker, “Classification with missing data in wireless sensor network,” IEEE Southeastcon, pp.533–538, April 2008.
  18. G.Y. Lu and D.W. Wong, “An adaptive inverse-distance weighting spatial interpolation technique,” J. Comput. Geosci.,vol.34, pp.1044–1055, 2008.
  19. M. Umer, L. Kulik and E. Tanin, “Kriging for localized spatial interpolation in sensor networks,” Int. Conf. on Scientific and Statistical Database Management, pp.525–532, 2008.
  20. X. Rong Li, Z. Zhao, "Measures of performance for evaluation of estimators and filters", Signal and Digital Processing Conf., pp.1-12, 2001.
  21. A. Azadeh, S.M. Asadzadeh, R.J. Marandi, S.N. Shirkouhi, G.B. Khoshjhou and S. Talebi, “Optimal estimation of missing values in randomized complete block design by genetic algorithm,” Knowledge-Based Systems, Elsevier, vol.37, pp.37–47, 2013.
  22. F. Xia, X. Kong and Z. Xu, “Cyber-physical control over wireless sensor and actuator networks with packet loss,” Wireless Networking Based Control, Springer, pp.85–102, 2011.
  23. K. Chen and S. Lien, "M2M Communications: Technologies and challenges", Elsevier Ad Hoc Networks, 2013.
  24. R.H. Choi, S.C. Lee, D.H. Lee and J. Yoo, “WiP abstract: Packet loss compensation for cyber-physical control systems,” IEEE/ACM Int. Conf. on Cyber-Physical Systems (ICCPS), pp.205, 2012.
  25. Y. Ke, J. Cheng and J.X. Yu, “Efficient discovery of frequent correlated subgraph pairs,” IEEE Int. Conf. on Data Mining (ICDM), pp.239–248, 2009.
  26. C.J. Wilmott and K. Matsuura, “Advantages of the mean absolute error (MAE) over root means square (RMSE) in assessing average model performance, ”Climate Research, vol.30, pp.79–82, 2005.
Index Terms

Computer Science
Information Sciences


Data recovery Cyber-physical systems error refinement deterministic patterns.