A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search
Volume 5 Issue 1
Jan.  2018

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77， TOP 5
Turn off MathJax
Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2018  >  5(1): 83-91
Xiaoxia Song and Yong Li, "Data Gathering in Wireless Sensor Networks Via Regular Low Density Parity Check Matrix," IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 83-91, Jan. 2018. doi: 10.1109/JAS.2017.7510448
Citation: Xiaoxia Song and Yong Li, "Data Gathering in Wireless Sensor Networks Via Regular Low Density Parity Check Matrix," IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 83-91, Jan. 2018. doi: 10.1109/JAS.2017.7510448
shu

Data Gathering in Wireless Sensor Networks Via Regular Low Density Parity Check Matrix

doi: 10.1109/JAS.2017.7510448

  • Department of Computer Science, Shanxi Datong University, Datong 037009, China

Funds:

the National Natural Science Foundation of China 61307121

ABRP of Datong 2017127

the Ph.D.'s Initiated Research Projects of Datong University 2013-B-17

the Ph.D.'s Initiated Research Projects of Datong University 2015-B-05

More Information
    Abstract
  • A great challenge faced by wireless sensor networks (WSNs) is to reduce energy consumption of sensor nodes. Fortunately, the data gathering via random sensing can save energy of sensor nodes. Nevertheless, its randomness and density usually result in difficult implementations, high computation complexity and large storage spaces in practical settings. So the deterministic sparse sensing matrices are desired in some situations. However, it is difficult to guarantee the performance of deterministic sensing matrix by the acknowledged metrics. In this paper, we construct a class of deterministic sparse sensing matrices with statistical versions of restricted isometry property (StRIP) via regular low density parity check (RLDPC) matrices. The key idea of our construction is to achieve small mutual coherence of the matrices by confining the column weights of RLDPC matrices such that StRIP is satisfied. Besides, we prove that the constructed sensing matrices have the same scale of measurement numbers as the dense measurements. We also propose a data gathering method based on RLDPC matrix. Experimental results verify that the constructed sensing matrices have better reconstruction performance, compared to the Gaussian, Bernoulli, and CSLDPC matrices. And we also verify that the data gathering via RLDPC matrix can reduce energy consumption of WSNs.

     

    • FullText(HTML)
  • loading
    • References(32)
  • [1]
    R. S. Dilmaghani, H. Bobarshad, M. Ghavami, S. Choobkar, and C. Wolfe, "Wireless sensor networks for monitoring physiological signals of multiple patients, " IEEE Trans. Biomed. Circ. Syst., vol. 5, no. 4, pp. 347-356, Aug. 2011. http://ieeexplore.ieee.org/document/5738699/
    [2]
    C. Perera, A. Zaslavsky, P. Christen, and D. Georgakopoulos, "Context aware computing for the internet of things: A survey, " IEEE Commun. Surv. Tutor., vol. 16, no. 1, pp. 414-454, Jan. -Mar. 2014. http://ieeexplore.ieee.org/document/6512846/
    [3]
    X. J. Ding, Y. Tian, and Y. Yu, "A real-time big data gathering algorithm based on indoor wireless sensor networks for risk analysis of industrial operations, " IEEE Trans. Ind. Inf., vol. 12, no. 3, pp. 1232-1242, Jun. 2016. http://ieeexplore.ieee.org/document/7111303/
    [4]
    D. Takaishi, H. Nishiyama, N. Kato, and R. Miura, "Toward energy efficient big data gathering in densely distributed sensor networks, " IEEE Trans. Emerg. Top. Comp., vol. 2, no. 3, pp. 388-397, Sep. 2014. doi: 10.1109/TETC.2014.2318177
    [5]
    C. Luo, F. Wu, J. Sun, and C. W. Chen, "Efficient measurement generation and pervasive sparsity for compressive data gathering, " IEEE Trans. Wirel. Commun., vol. 9, no. 12, pp. 3728-3738, Dec. 2010. http://ieeexplore.ieee.org/document/5595724
    [6]
    A. F. Liu, L. X. Cai, T. H. Luan, and A. Ranabahu, "QoS-aware data collection in wireless sensor networks, " Int. J. Distrib. Sensor Netw., vol. 2015, pp. Article ID 769083, 2015. doi: 10.1155/2015/769083
    [7]
    S. Lindsey, C. Raghavendra, and K. M. Sivalingam, "Data gathering algorithms in sensor networks using energy metrics, " IEEE Trans. Parall. Distrib. Syst., vol. 13, no. 9, pp. 924-935, Sep. 2002. http://ieeexplore.ieee.org/document/1036066/
    [8]
    M. Khan, G. Pandurangan, and V. S. A. Kumar, "Distributed algorithms for constructing approximate minimum spanning trees in wireless sensor networks, " IEEE Trans. Parall. Distrib. Syst., vol. 20, no. 1, pp. 124-139, Jan. 2009. http://ieeexplore.ieee.org/document/4492767/
    [9]
    Y. S. Liu and Z. Wang, "A prediction-based data collection method in wireless sensor network using Kalman filter, " ICIC Express Lett., vol. 2. no. 6, pp. 1439-1446, Dec. 2011. https://www.mendeley.com/research-papers/predictionbased-data-collection-method-wireless-sensor-network-using-kalman-filter/
    [10]
    O. Younis and S. Fahmy, "HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks, " IEEE Trans. Mob. Comp., vol. 3, no. 4, pp. 366-379, Oct. -Dec. 2004. http://ieeexplore.ieee.org/document/1347100/
    [11]
    H. Lin and H. Uster, "Exact and heuristic algorithms for data-gathering cluster-based wireless sensor network design problem, " IEEE/ACM Trans. Netw., vol. 22, no. 3, pp. 903-916, Jun. 2014. http://ieeexplore.ieee.org/document/6516992/
    [12]
    X. X. Song and G. M. Shi, "Data gathering of WSNs based on sequential compressed sensing and sparse sensing, " Int. Rev. Comp. Softw., vol. 7, no. 1, pp. 397-402, Jan. 2012. https://www.mendeley.com/research-papers/data-gathering-wsns-based-sequential-compressed-sensing-sparse-sensing/
    [13]
    D. L. Donoho, "Compressed sensing, " IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289-1306, Apr. 2006. http://ieeexplore.ieee.org/document/1614066/
    [14]
    E. J. Candes and M. B. Wakin, "An introduction to compressive sampling, " IEEE Signal Process. Mag., vol. 25, no. 2, pp. 21-30, Mar. 2008.
    [15]
    A. Amini and F. Marvasti, "Deterministic construction of binary, bipolar, and ternary compressed sensing matrices, " IEEE Trans. Inf. Theory, vol. 57, no. 4, pp. 2360-2370, Apr. 2011. http://ieeexplore.ieee.org/document/5730553/
    [16]
    S. Jafarpour, W. Y. Xu, B. Hassibi, and R. Calderbank, "Efficient and robust compressed sensing using optimized expander graphs, " IEEE Trans. Inf. Theory, vol. 55, no. 9, pp. 4299-4308, Sep. 2009. http://ieeexplore.ieee.org/document/5208528/
    [17]
    H. F. Zheng, F. Yang, X. H. Tian, X. Y. Gan, X. B. Wang, and S. L. Xiao, "Data gathering with compressive sensing in wireless sensor networks: a random walk based approach, " IEEE Trans. Parall. Distrib. Syst., vol. 26, no. 1, pp. 35-44, Jan. 2015. http://ieeexplore.ieee.org/document/6748092/?arnumber=6748092
    [18]
    L. Applebaum, S. D. Howard, S. Searle, and R. Calderbank, "Chirp sensing codes: deterministic compressed sensing measurements for fast recovery, " Appl. Comput. Harm. Anal., vol. 26, no. 2, pp. 283-290, Mar. 2009. http://www.sciencedirect.com/science/article/pii/S1063520308000869
    [19]
    N. Ailon and E. Liberty, "Fast dimension reduction using Rademacher series on dual BCH codes, " in Proc. 19th Annu. ACM-SIAM Symp. Discrete Algorithms (SODA), San Francisco, California, USA, 2008, pp. 1-9. doi: 10.1007/s00454-008-9110-x
    [20]
    J. Haupt, W. U. Bajwa, G. Raz, and R. Nowak, "Toeplitz compressed sensing matrices with applications to sparse channel estimation, " IEEE Trans. Inf. Theory, vol. 56, no. 11, pp. 5862-5875, Nov. 2010. http://ieeexplore.ieee.org/document/5605341
    [21]
    R. Calderbank, S. Howard, and S. Jafarpour, "Construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property, " IEEE J. Sel. Top. Signal Process., vol. 4, no. 2, pp. 358-374, Apr. 2010. http://ieeexplore.ieee.org/document/5419073/
    [22]
    S. Hong, H. Park, B. Shin, J. S. No, and H. Chung, "A new performance measure using k-set correlation for compressed sensing matrices, " IEEE Signal Process. Lett., vol. 19, no. 3, pp. 143-146, Mar. 2012. http://ieeexplore.ieee.org/document/6125989/
    [23]
    R. Berinde, A. C. Gilbert, P. Indyk, H. Karloff, and M. J. Strauss, "Combining geometry and combinatorics: A unified approach to sparse signal recovery, " in Proc. 46th Annu. Allerton Conf. Communication, Control, and Computing, Urbana-Champaign, IL, USA, 2008, pp. 798-805. http://ieeexplore.ieee.org/document/4797639/
    [24]
    D. Baron, S. Sarvotham, and R. G. Baraniuk, "Bayesian compressive sensing via belief propagation, " IEEE Trans. Signal Process., vol. 58, no. 1, pp. 269-280, Jan. 2010. http://ieeexplore.ieee.org/document/5169989
    [25]
    W. Wang, M. J. Wainwright, and K. Ramchandran, "Information-theoretic limits on sparse signal recovery: dense versus sparse measurement matrices, " IEEE Trans. Inf. Theory, vol. 56, no. 6, pp. 2967-2979, Jun. 2010. http://ieeexplore.ieee.org/document/5466548/
    [26]
    E. J. Candes and T. Tao, "Near-optimal signal recovery from random projections: Universal encoding strategies?, " IEEE Trans. Inf. Theory, vol. 52, no. 12, pp. 5406-5425, Dec. 2006. http://ieeexplore.ieee.org/document/4016283/
    [27]
    R. Gallager, "Low-density parity-check codes, " IEEE Trans. Inf. Theory, vol. 8, no. 1, pp. 21-28, Jan. 1962. http://ieeexplore.ieee.org/document/1057683/?arnumber=1057683
    [28]
    S. Lin and D. J. Costello Jr, Error Control Coding. 2nd ed. New York, USA:Prentice Hall, 2004.
    [29]
    M. J. Wainwright, E. Maneva, and E. Martinian, "Lossy source compression using low-density generator matrix codes: Analysis and algorithms, " IEEE Trans. Inf. Theory, vol. 56, no. 3, pp. 1351-1368, Mar. 2010.
    [30]
    L. Gan, C. Ling, T. T. Do, and T. D. Tran, "Analysis of the statistical restricted isometry property for deterministic sensing matrices using Stein's method, "[Online]. Available: http://dsp.rice.edu/sites/dsp.rice.edu/files/cs/GanStatRIP.pdf, September 22, 2017.
    [31]
    D. L. Donoho and Y. Tsaig, "Fast solution of l1-norm minimization problems when the solution may be sparse, " IEEE Trans. Inf. Theory, vol. 54, no. 11, pp. 4789-4812, Nov. 2008. http://ieeexplore.ieee.org/document/4655448/
    [32]
    M. S. Asif and J. Romberg, "Dynamic updating for l1 minimization, " IEEE J. Sel. Top. Signal Process., vol. 4, no. 2, pp. 421-434, Apr. 2010. http://www.oalib.com/paper/3821091
    • Related Articles
    • Supplements(0)

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(5)  / Tables(6)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (1402) PDF downloads(68) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return