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http://dx.doi.org/10.13067/JKIECS.2016.11.11.1069

Traffic Estimation Method for Visual Sensor Networks  

Park, Sang-Hyun (Dept. of Multimedia Engineering, Sunchon National University)
Publication Information
The Journal of the Korea institute of electronic communication sciences / v.11, no.11, 2016 , pp. 1069-1076 More about this Journal
Abstract
Recent development in visual sensor technologies has encouraged various researches on adding imaging capabilities to sensor networks. Video data are bigger than other sensor data, so it is essential to manage the amount of image data efficiently. In this paper, a new method of video traffic estimation is proposed for efficient traffic management of visual sensor networks. In the proposed method, a first order autoregressive model is used for modeling the traffic with the consideration of the characteristics of video traffics acquired from visual sensors, and a Kalman filter algorithm is used to estimate the amount of video traffics. The proposed method is computationally simple, so it is proper to be applied to sensor nodes. It is shown by experimental results that the proposed method is simple but estimate the video traffics exactly by less than 1% of the average.
Keywords
Visual Sensor Network; Traffic Modeling; Traffic Estimation; Video Traffic Management;
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Times Cited By KSCI : 3  (Citation Analysis)
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1 B. Tavli, K. Bicakci, R. Zilan, and J. Barcelo-Ordinas, "A survey of visual sensor network platforms," Multimededia Tools and Applications, vol. 60, no. 3, 2012, pp. 689-726.   DOI
2 J. Zhang, Q. Xiang, Y. Yin, C. Chen, and X. Luo, "Adaptive compressed sensing for wireless image sensor networks," Multimedia Tools and Applications, vol. 64, 2016, pp. 1-16.
3 P. Porambage, A. Heikkinen, E. Harjula, A. Gurtov, and M. Ylianttila, "Quantitative Power Consumption Analysis of a Multi-tier Wireless Multiemedia Sensor Network," In Proc. European Wireless 2016, Oulu, Finland, May 2016, pp. 1-6.
4 J. Park, S. Lee, and W. Oh, "Congestion Control Mechanism for Efficient Network Environment in WMSN," J. of the Korea Institute of Electronic Communication Sciences, vol. 10, no. 2, 2015, pp. 289-296.   DOI
5 K. Lee, Y. Kim, and H. Lee, "Receive Prediction based Period Adaptive Wakeup Technique for WSN," J. of the Korea Institute of Electronic Communication Sciences, vol. 10, no. 11, 2015, pp. 1265-1270.   DOI
6 T. Little, J. Konrad, and P. Ishwar, "A wireless video sensor network for autonomous coastal sensing," In Proc. Conf. on Coastal Environmental Sensing Networks, Boston, USA, Apr. 2007, pp. 1-5.
7 K. Nam, "A Study on Yeong-sna River Ecological Environment Monitoring based on IoT," J. of the Korea Institute of Electronic Communication Sciences, vol. 10, no. 2, 2015, pp. 203-210.   DOI
8 M. Chen, S. Gonzalez, H. Cao, Y. Zhang, and S. Vuong, "Enabling low bit-rate and reliable video surveillance over practical wireless sensor network," The J. of Supercomputing, vol. 65, no. 1, 2013, pp. 287-300.   DOI
9 G. Kirchgassner, J. Wolters, and U. Hassler, Introduction to modern time series analysis. Berlin: Germany:Springer Science & Business Media, 2012.
10 D. Simon, Optimal state estimation: Kalman, H infinity, and nonlinear approaches. Hoboken, Jew Jersey: John Wiley & Sons, 2006.