Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Real-time Error Detection Based on Time Series Prediction for Embedded Sensors

임베디드 센서를 위한 시계열 예측 기반 실시간 오류 검출 기법

  • Kim, Hyung-Il (Department of Multimedia, Korea Nazarene University)
  • 김형일 (나사렛대학교 멀티미디어학과)
  • Received : 2011.10.17
  • Accepted : 2011.11.18
  • Published : 2011.12.31
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Abstract

An embedded sensor is significantly influenced by its spatial environment, such as barriers or distance, through low power and signal strength. Due to these causes, noise data frequently occur in an embedded sensor. Because the information acquired from the embedded sensor exists in a time series, it is hard to detect an error which continuously takes place in the time series information on a realtime basis. In this paper, we proposes an error detection method based on time-series prediction that detects error signals of embedded sensors in real time in consideration of the physical characteristics of embedded devices. The error detection method based on time-series prediction proposed in this paper determines errors in generated embedded device signals using a stable distance function. When detecting errors by monitoring signals from an embedded device, the stable distance function can detect error signals effectively by applying error weight to the latest signals. When detecting errors by monitoring signals from an embedded device, the stable distance function can detect error signals effectively by applying error weight to the latest signals.

임베디드 센서는 낮은 전력량과 신호의 세기로 장애물이나 거리와 같은 공간 환경에 많은 영향을 받으며, 이러한 원인들로 인해 임베디드 센서에서는 노이즈 데이터가 빈번히 발생한다. 임베디드 센서에서 획득하는 정보는 시계열 데이터로 존재하기 때문에 지속적으로 발생하는 시계열 정보에 대한 오류 검출을 실시간적으로 수행하기는 어렵다. 본 논문에서는 임베디드 장치의 물리적 특성을 고려하여 실시간적으로 발생하는 임베디드 센서의 오류 신호를 검출하는 시계열 예측 기반 오류 검출 기법을 제안한다. 본 논문에서 제안한 시계열 예측 기반 오류 검출 기법은 안정 구간 함수를이용하여 현재 발생하는 임베디드장치 신호의 오류를 판단한다. 안정 구간 함수는 임베디드장치 신호를 관측하여 오류 검출을 수행할 때 최근의 신호들에 오류 가중화를 적용함으로써 효과적으로 오류 신호를 탐지할 수 있다. 본 논문에서 제안한 기법을 Intel Lab 신호를 이용하여 실험하였으며, 실험에서 본 논문에서 제안한 기법은 중심이동평균 기법에 비해 26.25%의 정확도 향상을 나타내었다.

Keywords

References

  1. S. Son, J. Lee, "Design and Implementation of Virtual Machine Monitor for Embedded Systems," Journal of The Korea Society of Computer and Information, Vol. 14, No. 1, pp. 57-64, Jan. 2009.
  2. K. Jung, W. Choi, "Performance Analysis of RS codes for Low Power Wireless Sensor Networks," Journal of The Korea Society of Computer and Information, Vol. 15, No. 4, pp. 83-90, Apr. 2010. https://doi.org/10.9708/jksci.2010.15.4.083
  3. S. Han, "A Robust Pair-wise Key Agreement Scheme based on Multi-hop Clustering Sensor Network Environments," Journal of The Korea Society of Computer and Information, Vol. 16, No. 3, pp. 251-260, Mar. 2011. https://doi.org/10.9708/jksci.2011.16.3.251
  4. M. A. M. Vieira, C. N. Coelho, D. C. da Silva, J. M. da Mata, "Survey on Wireless Sensor Network Devices," In Proceedings of IEEE Conference on Emerging Technologies and Factory Automation, Vol. 1, pp. 537-544, Sept. 2003.
  5. M. M. Gaber, "Learning from Data Streams: Processing Techniques in Sensor Network," Springer Verlag, 2007.
  6. S. Subramaniam, T. Palpanas, D. Papadopoulos, V. Kalogerakiand, and D. Gunopulos, "Online Outlier Detection in Sensor Data using Nonparametric Models," In Proceedings of International Conference on Very Large Data Bases, pp. 187-198, Sept. 2006.
  7. J. Han and M. Kamber, "Data Mining: Concepts and Techniques," Morgan Kaufmann, 2006.
  8. J. Chen, S. Kher, and A. Somani, "Distributed Fault Detection of Wireless Sensor Networks," In Proceedings of the 2006 Workshop on Dependability Issues in Wireless Ad Hoc Networks and Sensor Networks, pp. 65-72, Sept. 2006.
  9. M. Ding, D. Chen, K. Xing, and X. Cheng, "Localized Fault-Tolerant Event Boundary Detection in Sensor Networks," In Proceedings of IEEE Conference of Computer and Communications Societies, Vol. 2, pp. 902-913, Mar. 2005.
  10. G. Tolle, J. Polastre, R. Szewczyk, D. Culler, N. Turner, K. Tu, S. Burgess, T. Dawson, P. Buonadonna, D. Gay, and W. Hong, "A Macroscope in the Redwoods," In Proceedings of the 3rd International Conference on Embedded Networked Sensor Systems, pp. 51-63, Nov. 2005.
  11. Y. Zhang, N. Meratnia, and P. Havinga, "Outlier Detection Techniques For Wireless Sensor Networks: A Survey," IEEE Communications Surveys & Tutorials, Vol. 12, No. 2, Apr. 2010.
  12. S. Rajasegarar, C. Leckie, M. Palaniswami, and J.C. Bezdek, "Distributed Anomaly Detection in Wireless Sensor Networks," In Proceedings of the 10th IEEE International Conference on Communication Systems, pp. 1-5, Oct. 2006.
  13. S. R. Jeffery, G. Alonso, M. J. Franklin, W. Hong, and J. Widom. "Declarative support for sensor data cleaning," Lecture Notes in Computer Science In 4th International Conference on Pervasive Computing, Vol. 3968, pp. 83-100, May 2006.
  14. G. Werner-Allen, K. Lorincz, J. Johnson, J. Lees, and M. Welsh, "Fidelity and yield in a volcano monitoring sensor network," In Proceedings of the 7th USENIX Symposium on Operating System Design and Implementation, pp. 381-396, Nov. 2006.
  15. K. Ni, N. Ramanathan, M. N. H. Chehade, L. Bal zano, S. Nair, S. Zahedi, E. Kohler, G. Pottie, M. Hansen, and M. Srivastava, "Sensor Network Data Fault Types," In Journal of ACM Transactions on Sensor Networks, Vol. 5, Issue 3, pp. 1-29, Aug. 2009.
  16. P. Buonadonna, D. Gay, J. M. Hellerstein, W. Hon g, and S. Madden, "Task: Sensor network in a box," In Proceedings of the 2nd European Workshop on Wireless Sensor Networks, pp. 133-144, Feb. 2005.
  17. E. Elnahrawy and B. Nath, "Cleaning and Querying Noisy Sensors," In Proceedings of International Workshop on Wireless Sensor Networks and Applications, pp. 78-87, Sept. 2003.
  18. M. C. Jun, H. Jeong, and C. C. J. Kuo, "Distributed Spatio-Temporal Outlier Detection in Sensor Networks," In Proceedings of SPIE, Vol.5819, pp. 273-284, Mar. 2006.
  19. K. Zhang, S. Shi, H. Gao, and J. Li, "Unsupervised Outlier Detection in Sensor Networks using Aggregation Tree," In Proceedings of Advanced Data Mining and Applications, pp. 158-169, Aug. 2007.
  20. B. Sheng, Q. Li, W. Mao, and W. Jin, "Outlier Detec tion in Sensor Networks," In Proceedings of 8th ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 219-228, Sept. 2007.
  21. K. Ni and G. Pottie, "Bayesian Selection of Non-Fau lty Sensors," In IEEE International Sympos ium on Information Theory, pp. 616-620, Jun. 2007.
  22. J. Branch, B. Szymanski, C. Giannella, R. Wolff, and H. Kargupta, "In-Network Outlier Detection in Wireless Sensor Networks," In Proceedings of the 26th IEEE International Conference on Distributed Computing Systems, pp. 51-58, Jul. 2006
  23. V. S. Kumar Samparthi, Harsh K. Verma, "Outlier Detection of Data in Wireless Sensor Networks Using Kernel Density Estimation," International Journal of Computer Applications, Vol. 5, No. 7, pp. 28-32, Aug. 2010.

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