YAN Wenjie, DONG Yongfeng, ZHANG Suqi, et al., “An Optimal CDG Framework for Energy Efficient WSNs,” Chinese Journal of Electronics, vol. 26, no. 1, pp. 137-144, 2017, doi: 10.1049/cje.2016.10.017
Citation: YAN Wenjie, DONG Yongfeng, ZHANG Suqi, et al., “An Optimal CDG Framework for Energy Efficient WSNs,” Chinese Journal of Electronics, vol. 26, no. 1, pp. 137-144, 2017, doi: 10.1049/cje.2016.10.017

An Optimal CDG Framework for Energy Efficient WSNs

doi: 10.1049/cje.2016.10.017
Funds:  This work is supported by the Natural Science Foundation of Hebei Province of China (No.F2015202214), the Tianjin Application Foundation and Advanced Technology Research Program (No.15JCQNJC00600, No16JCQNJC00400), the National Natural Science Foundation of China (No.61201310, No.61174016), and Science and Technology Program of Hebei Province of China (No.15210506).
  • Received Date: 2014-12-11
  • Rev Recd Date: 2015-05-05
  • Publish Date: 2017-01-10
  • Compressed sensing (CS) has been applied widely in Wireless sensor networks (WSNs) recently. An optimal Compressed data gathering (CDG) framework for energy efficient WSNs is proposed here. A novel Measurement matrix optimization algorithm (MMOA) is proposed for compressed data measurement in WSNs. Diffusion wavelet transform matrix (DWTM) is chosen for sparse representation of the compressed data. An Optimal data aggregation tree (ODAT) algorithm is presented based on CS and routing technology. MMOA is to reduce the data transmissions under the same data reconstruction ratio. DWTM is to make the original data become more sparse and to increase the compressed data reconstruction ratio. The main purpose of ODAT is to minimize the energy consumption of the whole WSNs through the CDG technology and the optimal route. We validate the efficiency of the proposed CDG framework based on MMOA, DWTM and ODAT through extensive experiments.
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  • W. Liu, Y.H. Zhu and J. Pan, "A grid-based routing algorithm with cross-level transmission to prolong lifetime of wireless sensor networks", Chinese Journal of Electronics, Vol.19, No.3, pp.499-502, 2010.
    X. Cui, S. Yang, X. Zhang, et al., "A novel neighboring propagation algorithm based on hierarchical routing scheme for power constrained wireless sensor networks", Chinese Journal of Electronics, Vol.21, No.2, pp.327-331, 2012.
    Y. Gu, Y. Ji, H. Chen, et al., "TAPEMAN:Towards an optimal data gathering mechanism in wireless sensor networks", Chinese Journal of Electronics, Vol.19, No.4, pp.594-598, 2010.
    S. Cheng, J. Li, Q. Ren, et al., "Bernoulli sampling based (∈, δ)-approximate aggregation in large-scale sensor networks", Proc. of the IEEE Conference on Information Communications, SanDiego, California, USA, pp.1181-1189, 2010.
    D. Slepian and J. Wolf. "Noiseless coding of correlated information sources", IEEE Transactions on Information Theory, Vol.19, No.4, pp.471-480, 1973.
    S. He, J. Chen, D. K. Yau, et al., "Cross-layer optimization of correlated data gathering in wireless sensor networks", IEEE Transactions on Mobile Computing, Vol.11, No.11, pp.1678-1691, 2012.
    H. Gupta, V. Navda, S. Das, et al., "Efficient gathering of correlated data in sensor networks", ACM Transactions on Sensor Networks, Vol.4, No.1, pp.1-31, 2008.
    D. Gong and Y. Yang, "Low-latency SINR-based data gathering in wireless sensor networks", Proc. of the IEEE Conference on Information Communications, Turin, Italy, pp.1941-1949, 2013.
    D. Gong and Y. Yang, "Low-latency SINR-based data gathering in wireless sensor networks", IEEE Transactions on Wireless Communications, Vol.13, No.6, pp.3207-3221, 2014.
    M. Malloy and R. Nowak, "Near-optimal adaptive compressed sensing", IEEE Transactions on Information Theory, Vol.60, No.7, pp.4001-4012, 2014.
    J. Tan, D. Carmon and D. Baron, "Signal estimation with additive error metrics in compressed sensing", IEEE Transactions on Information Theory, Vol.60, No.1, pp.150-158, 2014.
    Y. Tang, B. Zhang, T. Jing, et al., "Robust compressive data gathering in wireless sensor networks", IEEE Transactions on Wireless Communications, Vol.12, No.6, pp.2754-2761, 2013.
    C. Zhao, W. Zhang, X. Yang, et al., "A novel compressive sensing based data aggregation scheme for wireless sensor networks", Proc. of IEEE International Conference on Communications, Sydney, Australia, pp.18-23, 2014.
    E.J. Candès, J. Romberg and T. Tao, "Robust uncertainty principles:Exact signal reconstruction from highly incomplete frequency information", IEEE Transactions on Information Theory, Vol.52, No.2, pp.489-509, 2006.
    M. Elad, "Optimized projections for compressed sensing", IEEE Transactions on Signal Processing, Vol.55, No.12, pp.5695-5702, 2007.
    V. Abolghasemi, S. Ferdowsi and S. Sanei, "A gradient-based alternating minimization approach for optimization of the measurement matrix in compressive sensing", Signal Processing, Vol.92, No.4, pp.999-1009, 2012.
    J. Xu, Y. Pi and Z. Cao, "Optimized projection matrix for compressive sensing", EURASIP Journal on Advances in Signal Processing, Vol.2010, pp.1-8, 2010.
    J.A. Tropp and A.C. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit", IEEE Transaction on Information Theory, Vol.53, No.12, pp.4655-4666, 2007.
    L. Xiang, J. Luo and A. Vasilakos, "Compressed data aggregation for energy efficient wireless sensor networks", Proc. of IEEE Conference on Sensor, Mesh and Ad hoc Communications and Networks, Salt Lake City, Utah, USA, pp.46-54, 2011.
    L. Xiang, J. Luo and C. Rosenberg, "Compressed data aggregation:Energy-efficient and high-fidelity data collection", IEEE/ACM Transactions on Networking, Vol.21, No.6, pp.1722-1735, 2013.
    H. Huang and A. Makur, "Optimized measurement matrix for compressive sensing", Sampling Theory in Signal and Image Processing, Vol.12, No.1, pp.71-86, 2013.
    J.A. Tropp, I.S. Dhillon, R.W. Heath, et al., "Designing structured tight frames via an alternating projection method", IEEE Transactions on Information Theory, Vol.51, No.1, pp.188-209, 2005.
    J.M. Duarte-Carvajalino and G. Sapiro, "Learning to sense sparse signals:Simultaneous sensing matrix and sparsifying dictionary optimization", IEEE Transactions on Image Processing, Vol.18, No.7, pp.1395-1408, 2009.
    C. Sigg, T. Dikk and J. Buhmann, "Learning dictionaries with bounded self-coherence", IEEE Signal Processing Letters, Vol.19, No.12, pp.861-864, 2012.
    R.R. Coifman and M. Maggioni, "Diffusion wavelets", Applied and Computational Harmonic Analysis, Vol.21, No.1, pp.53-94, 2006.
    L. Xu, Y. Wang and C. Hu, "Redundancy control in large scale sensor networks via compressive sensing", Proc. of IEEE Conference on Control Conference, Xi'an, China, pp.7494-7498, 2013.
    C. Luo, F. Wu, J. Sun, et al., "Efficient measurement generation and pervasive sparsity for compressive data gathering", IEEE Transaction on Wireless Communications, Vol.9, No.12, pp.3728-3738, 2010.
    M.T. Nguyen and K.A. Teague, "Tree-based energy-efficient data gathering in wireless sensor networks deploying compressive sensing", Proc. of IEEE Conference on Wireless and Optical Communication, Newark, NJ, USA, pp.1-6, 2014.
    L.H. Chang and J.Y. Wu, "An improved RIP-based performance guarantee for sparse signal recovery via orthogonal matching pursuit", Proc. of IEEE Conference on Communications, Control and Signal Processing, Athens, Greece, pp.28-31, 2014.
    D.L. Donoho, "Compressed sensing", IEEE Transactions on Information Theory, Vol.52, No.4, pp.1289-1306, 2006.
    L. Welch, "Lower bounds on the maximum cross correlation of signals", IEEE Transactions on Information Theory, pp.397-399, 1974.
    T.H. Cormen, C.E. Leiserson, R.L. Rivest, et al., Introduction to Algorithms, MIT press, Cambridge, USA, 2001.
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