JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Validation of Sensing Data Based on Prediction and Frequency
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Validation of Sensing Data Based on Prediction and Frequency
Lee, SunYoung; Kim, Ki-Il;
  PDF(new window)
 Abstract
As wireless sensor networks become eligible as well as useful in several controled systems where surrounding environments are likely to be monitored, their stabilization become important research challenge. Generally, stabilization is mostly dependent on reliability of sensing value. To achieve such reliability in wireless sensor networks, the most of previous research work have tendency to deploy the same type of multiple sensor units on one node. However, these mechanisms lead to deployment problem by increasing cost of sensor node. Moreover, it may decrease reliability in the operation due to complex design. In order to solve this problem, in this paper, we propose a new validation scheme which is based on prediction and frequency value. In the proposed scheme, we take into exceptional cases account, for example, outbreak of fire. Finally, we demonstrate that the proposed scheme can detect abnormal sensing value more than 13 percent as compared to previous work through diverse simulation scenarios.
 Keywords
Wireless sensor networks;Reliability;Prediction;Frequency value;
 Language
Korean
 Cited by
 References
1.
Y. D. Lee, "Implementation of Greenhouse Environment Monitoring System based on Wireless Sensor Networks," Journal of the Korea Institute of Information and Communication Engineering, vol. 17, no. 11, pp. 2686-2692, Nov. 2013. crossref(new window)

2.
S. B. Jang, Gang, W. Shin, S. T. Hong, A. K. Lee, H. M. Park,"USN-based Water Treatment Plant Facilities Data anagement Techniques and Reliability," Journal of the Korea Institute of Information and Communication Engineering, vol. 17, no. 11, pp. 2736-2744, Nov. 2013. crossref(new window)

3.
I. S. Lee, J. H. Cho, "Real-Time Fault Diagnosis for Tin Oxide Gas Sensors Using Thermal Modulation and an ART-2 Neural Network," Journal of Korean Institute of Information Technology, vol.11, no.2, pp 27-36, Feb. 2013.

4.
S. U. Lee, "Neural Network for Software Reliability Prediction with Unnormalized Data," The Transactions of the Korea Information Processing Society, vol. 7, no. 5, pp. 1419-1425, May 2000..

5.
K. Shim, J. G. Yim, "Estimation of Reliability of a System Based on Two Typed Data," Journal of Korea Multimedia Society, vol. 16, no. 3, pp.336-341, Mar. 2013. crossref(new window)

6.
G. Y. Yoon, N. H. Kim, H. K. Choi , D. Y. Jung, S. H. Choi, G. T. Kim, "A Winter Road Weather Information System Using Ubiquitous Sensor Network," Journal of Korea Multimedia Society, vol. 14. no. 3, pp. 393-402, Mar. 2011.

7.
S. S. Lee, M. H. Song, K. H. Won, J. H. Kim. "An Efficient Method for Improving the Reliability of Sensing Data Using Multi-sensors in Wireless Sensor Network Systems," Journal of Information and Telecommunication Facility Engineering, vol. 8. no. 3, pp. 116-121, Sep. 2009.

8.
T. Ryutov, C. Neuman,"Trust based Approach for Improving Data Reliability in Industrial Sensor Networks," Journal of International Federation for Information Processing, vol. 238, pp. 349-365, Jul. 2007.

9.
W. G. Oh, S. K. Lee, "An Effective Algorithm for Diagnosing Sensor Node Faults," Journal of Korea Institute of Electronic Communication Science, vol. 10, no. 2, pp. 283-288, Feb. 2015. crossref(new window)