전기학회논문지P (The Transactions of the Korean Institute of Electrical Engineers P)

  • 제56권1호
  • /
  • Pages.28-36
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  • 2007
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  • 1229-800X(pISSN)
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  • 2586-7792(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
권호 표지

센서 네트워크 기반 이상 데이터 복원 시스템 개발

Design of A Faulty Data Recovery System based on Sensor Network

  • 김성호 (군산대학교 전자정보공학부) ;
  • 이영삼 (군산대학교 전자정보공학부) ;
  • 육의수 (군산대학교 전자정보공학부)
  • 발행 : 2007.03.01
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초록

Sensor networks are usually composed of tens or thousands of tiny devices with limited resources. Because of their limited resources, many researchers have studied on the energy management in the WSNs(Wireless Sensor Networks), especially taking into account communications efficiency. For effective data transmission and sensor fault detection in sensor network environment, a new remote monitoring system based on PCA(Principle Component Analysis) and AANN(Auto Associative Neural Network) is proposed. PCA and AANN have emerged as a useful tool for data compression and identification of abnormal data. Proposed system can be effectively applied to sensor network working in LEA2C(Low Energy Adaptive Connectionist Clustering) routing algorithms. To verify its applicability, some simulation studies on the data obtained from real WSNs are executed.

키워드

참고문헌

  1. B. Krishnamachari, D. Estrin and S. Wicker, 'Modelling Data-Centric Routing in Wireless Sensor Networks,' IEEE INFOCOM'02, June 2002
  2. W. Ye, J. Heidemann and D.Estrin 'An Energy-Efficient MAC Protocol for Wireless Sensor Networks' IEEE INFOCOM'02, June 2002
  3. S. Ranganathan, AD. George, R.W. Todd, Matthew C.Chidester, 'Gossip-Style Failure Detection and Distributed Consensus for Scalable Heterogeneous Clusters', HCS Research Laboratory, 2000
  4. M. Barborak, A. Dahbura, M. Malek, 'The Consensus problem in Fault-Tolerant Computing, ACM Computing Surveys', Vol 25, No 2, 171-220, 1993 https://doi.org/10.1145/152610.152612
  5. Ian F. Akyildiz, Weilian Su, Y. Sankarasubramaniam, Erdal Cayirci, 'A Survey on Sensor Networks', IEEE Communications Magazine, August 2002, pages 102-114
  6. MacGregor, J.F., K. Costas, 'Process monitoring and diagnosis by multiblock PLS method', AIChE J. , 40(5), 826-838, 1994 https://doi.org/10.1002/aic.690400509
  7. R. Isermann, 'Process Fault Detection Based on Modelling and Estimation Methods-A survey', Automatica, Vol. 20. 4, pp. 387-404, 1984 https://doi.org/10.1016/0005-1098(84)90098-0
  8. Dunia, R. 'Identification of faulty sensors using principle component analysis', AIChE J., 42(10), 2797-2812, 1996 https://doi.org/10.1002/aic.690421011
  9. Kramer, M.A., 'Autoassociative neural networks,' Computers in Chemical Eng., Vol.16, No.4, pp.313-328, 1992 https://doi.org/10.1016/0098-1354(92)80051-A
  10. J. W. Hines, D. J, Wrest, and R. E. Uhring, 'Plant Wide Sensor Calibration Monitoring:',published in the proceedings of The 1996 IEEE International Symposium on Intelligent Control, Sept. 15-18, pp.378-383,1996
  11. F. K. L. Dehni and Y. Bennani. Power control and clustering in wireless sensor networks. In Proceedings of Med-Hoc-Net, 2005
  12. J, Vesanto, J, Hirnberg, E. Alhoniemi, J, Parhankangas' SOM Toolbox for Matlab 5', Copyright (C) 2005. (http://www.cis.hut.fi/proiects/ somtoolbox/)