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Home > Archive>Volume 21, Issue 6, 2010 >1364-1377
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Haar Wavelet Data Compression Algorithm with Error Bound for Wireless Sensor Networks
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    Abstract:

    Wireless sensor networks usually have limited energy and transmission capacity, and they can’t match the transmission of a great deal of data. So, it is necessary to approximate or aggregate raw data sampled by sensors in networks. By designing an error tree and solving the regression equations set, this paper proposes a data compression scheme with infinite norm error bound for wireless sensor networks. The algorithms in the scheme can simultaneously explore the temporal and multiple-streams correlations among the sensory data. The temporal correlation in one stream is captured by the 1D Haar wavelet transform. For multivariate monitoring sensor networks, some streams from one sensor are selected as the bases according to the correlation coefficient matrix, and the other streams from the same sensor node can be expressed with one of these bases using linear regression. Theoretically and experimentally, it is concluded that the proposed algorithms can effectively exploit the temporal and multiple-streams correlations on the same sensor node and achieve significant data reduction.

    Reference
    [1] Li JZ, Gao H. Survey on sensor network research. Journal of Computer Research and Development, 2008,45(1):1-15 (in Chinese with English abstract).
    [2] Kimura N, Latifi S. A survey on data compression in wireless sensor networks. In: Proc. of the Int’l Conf. on Information Technology: Coding and Computing, Vol.2. Piscataway: IEEE Press, 2005. 8-13.
    [3] Deligiannakis A, Kotidis Y, Roussopoulos N. Dissemination of compressed historical information in sensor networks. The VLDB Journal, 2007,16(4):439-461. [doi: 10.1007/s00778-005-0173-5]
    [4] Chu D, Deshpande A, Hellerstein JM, Hong W. Approximate data collection in sensor networks using probabilistic models. In: Proc. of the 22nd Int’l Conf. on Data Engineering. Piscataway: IEEE Press, 2006. 48-59.
    [5] Kanagal B, Deshpande A. Online filtering, smoothing and probabilistic modeling of streaming data. In: Proc. of the IEEE 24th Int’l Conf. on Data Engineering. Piscataway: IEEE Press, 2008. 1160-1169.
    [6] Najafi H, Lahouti F, Shiva M. AR modeling for temporal extension of correlated sensor network data. In: Proc. of the Int’l Conf. on Software in Telecommunications and Computer Networks. Piscataway: IEEE Press, 2006. 117-120.
    [7] Tulone D, Madden S. PAQ: Time series forecasting for approximate query answering in sensor networks. In: R?mer K, Karl H, Mattern F, eds. Proc. of the Wireless Sensor Networks (EWSN 2006). LNCS 3868, Berlin: Springer-Verlag, 2006. 21-37.
    [8] Borgne YL, Bontempi G. Unsupervised and supervised compression with principal component analysis in wireless sensor networks. In: Ganguly AR, et al., eds. Proc. of the Knowledge Discovery from Sensor Data (Sensor-KDD 2007). Boca Raton: CRC Press, 2008.
    [9] Ganesan D, Estrin D, Heidemann J. DIMENSIONS: Why do we need a new data handling architecture for sensor networks? SIGCOMM Computer Communication Review, 2003,33(1):143-148. [doi: 10.1145/774763.774786]
    [10] Chen HM, Li J, Mohapatra P. RACE: Time series compression with rate adaptive and error bound for sensor networks. In: Proc. of the 2004 IEEE Int’l Conf. on Mobile Ad-Hoc and Sensor Systems. Piscataway: IEEE Press, 2004. 124-133.
    [11] Ciancio A, Pattem S, Ortega A, Krishnamachari B. Energy-Efficient data representation and routing for wireless sensor networks based on a distributed wavelet compression algorithm. In: Proc. of the IPSN. New York: ACM Press, 2006. 309-316.
    [12] Zhou SW, Lin YP, Zhang JM, Ouyang JC, Lu XG. A wavelet data compression algorithm using ring topology for wireless sensor networks. Journal of Software, 2007,18(3):669-680 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/18/669. htm [doi: 10.1360/jos180669]
    [13] Garofalakis M, Gibbons PB. Wavelet synopses with error guarantees. In: Proc. of the ACM SIGMOD. New York: ACM Press, 2002. 476-487.
    [14] Pan LQ, Li JZ, Luo JZ. An approximate query processing algorithm with confidence based on model fitting in sensor networks. Journal of Computer Research and Development, 2008,45(1):73-82 (in Chinese with English abstract).
    [15] Chakrabarti K, Garofalakis M, Rastogi R, Shim K. Approximate query processing using wavelets. The VLDB Journal, 2001,10(3): 199-223.
    [16] Lazaridis I, Mehrotra S. Capturing sensor-generated time series with quality guarantees. In: Proc. of the 19th Int’l Conf. on Data Engineering. Piscataway: IEEE Press, 2003. 429-440.
    附中文参考文献: [1] 李建中,高宏.无线传感器网络的研究进展.计算机研究与发展,2008,45(1):1-15.
    [12] 周四望,林亚平,张建明,欧阳竞成,卢新国.传感器网络中基于环模型的小波数据压缩算法.软件学报,2007,18(3):669-680. http://www.jos.org.cn/1000-9825/18/669.htm [doi: 10.1360/jos180669]
    [14] 潘立强,李建中,骆吉洲.无线传感器网络中基于模型拟合的可信近似查询处理算法.计算机研究与发展,2008,45(1):73-82.
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张建明,林亚平,周四望,欧阳竞成.传感器网络中误差有界的小波数据压缩算法.软件学报,2010,21(6):1364-1377

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  • Revised:October 27,2008
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