Yongzhi Liu This email address is being protected from spambots. You need JavaScript enabled to view it.1,2, Xueping Jia3 and Dechang Pi1
1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P.R. China 2Department of Information Engineering, Xuancheng Vocational & Technical College, Xuancheng 242000, P.R. China 3Department of Architecture and Art, Xuancheng Vocational & Technical College, Xuancheng 242000, P.R. China
Received: September 7, 2015 Accepted: December 1, 2015 Publication Date: March 1, 2016
In order to reduce the power consumption of the sensor, the key points of the algorithm are proposed, which can greatly reduce the transmission data and reduce the power consumption; The Sink receives the key point sequence, and uses the piece-wise linear algorithm to fit the data, for the user to query, statistics and graphics and other operation; The empirical evidence of this algorithm fits the raw data well, Less computation, less transmission of data, is conducive to reduce the power consumption in the wireless sensor.
Keywords: Key Points, Piece-wise Linear Fitting, Wireless Sensor, Power Consumption
REFERENCES
[1] Zhong, W. J., Liu, M. Y. and Peng, G., “Research on Predictive Algorithm of Dynamic Power Managerment in Embeded System,” Microelectronics & Computer, Vol. 22, No. 11, pp. 5658(2005).
[2] Zhong, W. J. and Liu, M. Y., “An Algorithm to Predict Idel Period Based Og Grey Model for Dynamic Power Management,” Transactions of Beijing Institute of Technology, Vol. 25, No. 11, pp. 965967 (2005).
[3] Abbasian, A., Hatami, S., Afzali-kusha, A., et al., Event-Driven Dynamic Power Managerment Based on Wavelet Forcecasting Theory[C] Circuits and Systems, ISCAS’04. Proceedings pf the 2004 International Symposium, Vol. 5, pp. 325328 (2004). doi: 10.1109/IS CAS.2004.1329528
[4] Yu, G. Z., Peng, H., Hu, J. S., et al., “Piecewise Linear Representation of Time Series Data,” Computer Applications and Software, Vol. 24, No. 12, pp. 1718 (2007).
[5] Xiao, H. and Hu, Y. F., “Data Mining Based on Segmented Time Warping Distance in Time Serise Database,” Journal of Computer Research and Development, Vol. 42, No. 1, pp. 7278 (2005). doi: 10.1360/ crad20050110
[6] Liao, J., Zhou, Z. L., Kou, Y. X., et al., “Method for Time Series Segment Based on Important Point,” Computer Engineering and Applications, Vol. 47, No. 24, pp. 166170 (2011).
[7] Keogh, E., Lin, J. and Fu, A., HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence, Proc of the 5th IEEE Int’l Conf on Data Mining Newport Beach, USA: IEEE Press, pp. 226233 (2005). doi: 10.1109/ICDM.2005.79
[8] Ding, J. and Liu, D. P., “Energy Saving Algorithm for Data Collection of Wireless Sensor Networks in Mobile Sink Environments,” Journal of Beijing University of Posts and Telecommunications, Vol. 36, No. 5, pp. 5155 (2013).
[9] Qin, Y., Wang, W. D. and Leng, S. P., “CA-Based Energy-Efficient Routing Protocol for WSN,” Journal of University of Electronic Science and Technology of China, Vol. 44, No. 4, pp. 513518 (2015).
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