CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

A Novel Application of Extended Kalman Filter for Efficient Information Processing in Subsurfaces

by Dimple Juneja, Atul Sharma, A.K Sharma
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 17 - Number 2
Year of Publication: 2011
Authors: Dimple Juneja, Atul Sharma, A.K Sharma
10.5120/2192-2783

Dimple Juneja, Atul Sharma, A.K Sharma . A Novel Application of Extended Kalman Filter for Efficient Information Processing in Subsurfaces. International Journal of Computer Applications. 17, 2 ( March 2011), 28-32. DOI=10.5120/2192-2783

@article{ 10.5120/2192-2783,
author = { Dimple Juneja, Atul Sharma, A.K Sharma },
title = { A Novel Application of Extended Kalman Filter for Efficient Information Processing in Subsurfaces },
journal = { International Journal of Computer Applications },
issue_date = { March 2011 },
volume = { 17 },
number = { 2 },
month = { March },
year = { 2011 },
issn = { 0975-8887 },
pages = { 28-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume17/number2/2192-2783/ },
doi = { 10.5120/2192-2783 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:04:36.181400+05:30
%A Dimple Juneja
%A Atul Sharma
%A A.K Sharma
%T A Novel Application of Extended Kalman Filter for Efficient Information Processing in Subsurfaces
%J International Journal of Computer Applications
%@ 0975-8887
%V 17
%N 2
%P 28-32
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recent works indicates that innovative deployment of sensors in subsurfaces can beneficially support the production of oil and gas. The data which is sensed by such sensors is usually corrupted with noise. Filtering is desirable in such embedded systems in order to smooth out such fluctuations that otherwise would shorten the lifespan of sensors. This contribution presents a unique application of Kalman filtering technique for processing such sensitive information because sensor readings are usually imprecise due to strong variations in environment and also, computation has to be much more energy efficient than communication. Out of the various filtering algorithms available, we have chosen to apply Kalman filter, primarily because it works well both in theory and practice and moreover, it is able to minimize the variance of estimation error i.e. filters noise from the actual signal more accurately.

References
  1. A.D’Costa, V.Ramachandran and A.M.Sayeed, “Distributed Classification of Gaussian Space–Time Sources in Wireless Sensor Networks”, IEEE Journal on Selected Areas in Communications, Vol. 22, No. 6, August 2004.
  2. A.D'Costa and A.M.Sayeed, "Collaborative Signal Processing for Distributed Classification in Sensor Networks”, In the Second International Workshop on Information Processing in Sensor Networks (IPSN '03), Palo Alto, CA, April, 2003.
  3. B.Chen, L.Tong and P.K.Varshney, “Channel-Aware Distributed Detection in Wireless Sensor Networks”, IEEE Signal Processing Magazine, Vol. 23, No. 4, July 2006.
  4. C. A. Gonclaves, P. K. Harvey, and M. A. Lovell, “Prediction Of Petrophysical Parameter Logs Using A Multilayer Backpropagation Neural Network,” in Developments in Petrophysics: Geological Society Special Publication. London, U.K.: Geological Society, 1997, pp. 169–180
  5. D.Guo and X.Wang, “Dynamic Sensor Collaboration via Sequential Monte Carlo”, IEEE Journal on Selected Areas in Communications, Vol. 22, No. 6, August 2004.
  6. D.Li, K.Wong, Y.H.Hu and A.Sayeed, “Detection, Classification and Tracking of Targets”, IEEE Signal Processing Magazine, March 2002.
  7. F.Zhao and J.Shin and J.Reich, “Information-Driven Dynamic Sensor Collaboration for Tracking Applications”, IEEE Signal Processing Magazine, March 2002.
  8. F.Zhao, J.Liu; J.Liu, L.Guibas and J.Reich, “Collaborative Signal and Information Processing: an Information-Directed Approach”, Proceedings of IEEE, Vol. 91, No. 8, August 2003.
  9. G. Pottie, W. Kaiser. “Wireless Integrated Network Sensors.” Communications of the ACM, 43(5):51 - 58, May 2000.
  10. H.Qi, Y.Xu and X.Wang, “Mobile-Agent-Based Collaborative Signal and Information Processing in Sensor Networks”, Proceedings of the IEEE, Vol. 91, No. 8, August 2003.
  11. I.F.Akyildiz, W.Su, Y.Sankarasubramaniam and E.Cayirci, “A Survey on Sensor Networks”, IEEE Communications Magazine, August 2002.
  12. J.H.Kotecha, V.Ramachandran and A.M. Sayeed, “Distributed Multitarget Classification in Wireless Sensor Networks”, IEEE Journal on Selected Areas in Communications, Vol. 23, No. 4, April 2005.
  13. J.Xiao, A.Ribeiro, Z.Luo and G.B.Giannakis, “Distributed Compression-Estimation Using Wireless Sensor Networks”, IEEE Signal Processing Magazine, Vol. 23, No. 4, July 2006.
  14. Juneja Dimple, Sharma Atul & Sharma A.K., “A Query Driven Routing Protocol for Wireless Sensor Nodes in Subsurface”. International Journal of Engineering Science and Technology. Vol. 2 No. 6, July 2010, pp. 1836-1843
  15. Juneja Dimple, Sharma Atul, Kumar Punit, Iyengar S.S, & Sharma A.K, “A Novel and Efficient Algorithm for Deploying Mobile Sensors in Subsurface”. Computer and Information Science. Vol 3, No 2 (2010), pp.94-105.
  16. Juneja Dimple, Sharma Atul & Sharma A.K., ”On The Role Of Wireless Sensor Networks In Subsurface Exploration: An Overview”, 2009 IEEE International Advance Computing Conference (Iacc 2009) 3159-3162
  17. L.J.Guibas, “Sensing, Tracking, and Reasoning with Relations”, IEEE Signal Processing Magazine, March 2000.
  18. M.Cetin, L.Chen, J.W. Fisher III, A.T. Ihler, R.L.Moses, M. J.Wainwright and A.S. Willsky, “Distributed Signal Processing in Sensor Networks”, IEEE Signal Processing Magazine, Vol. 23, No. 4, July 2006.
  19. R.Brooks, P.Ramanathan and A.Sayeed, "Distributed Target Classification and Tracking in Sensor Networks”,Proceedings of the IEEE, Vol. 91, No. 8, August 2003.
  20. W.Bajwa, J. Haupt, A.Sayeed and R.Nowak, “Joint Source-Channel Communication for Distributed Estimation in Sensor Networks”, submitted to the IEEE Transactions on Information Theory, August 2006.
  21. Wilamowski Bogdan M, Kaynak Okyay,” Oil Well Diagnosis by Sensing Terminal Characteristics of the Induction Motor”, IEEE Transactions On Industrial Electronics, Vol. 47, No. 5, October 2000, 1100-1107 .
  22. XIAO, ZU-QI, South Huang Hai Oil Corp, “The Calculation of Oil Temperature in a Well”, 1987. An unpublished article submitted to Society of Petroleum Engineers http://www.onepetro.org/mslib/app/Preview.do?paperNumber=00017125&societyCode=SPE
  23. Dan Simon, “ Kalman Filtering” Embedded Systems Programming, June 2001, pp:72-79.
  24. Greg Welch , Gary Bishop, “ An Introduction to Kalman Filter” UNC-Chapel Hill,TR 95-041, July 24,2006, pp:1-16.
  25. Clayton M. Costa1, Cicília R. M. Leite2, Francisco M. M. Neto3, Pedro F. R. Neto, “AMSO: Real-Time System For Oil Wells Onshores Using Sensors Network” avaialble online at www.eatis.org/eatis2010/portal/paper/memoria/html/files/39.pdf
  26. Pal Skalle, Agnar Aamodt, “Knowledge-Based Decision Support In Oil Well Drilling”, Intelligent Information Processing II: IFIP International Conference on Intelligent Information Processing, pp:443-455
Index Terms

Computer Science
Information Sciences

Keywords

Wireless Sensor Networks Kalman Filtering Algorithm Extended Kalman Filter Information Processing Estimation Error

Powered by PhDFocusTM