Vol. 70

Front:[PDF file] Back:[PDF file]
Latest Volume
All Volumes
All Issues
2018-07-13

An Alternation Diffusion LMS Estimation Strategy Over Wireless Sensor Network

By Lin Li and Donghui Li
Progress In Electromagnetics Research M, Vol. 70, 135-143, 2018
doi:10.2528/PIERM18042302

Abstract

This paper presents a distributed estimation strategy called alternation diffusion LMS estimation (AD-LMS) to estimate an unknown parameter of interests from noisy measurement over wireless sensor network. It is useful in the wireless sensor networks where robustness and low consumption are desired features. Diffusion LMS is introduced in this estimation strategy to improve the performance and reduce the communication burden. With the proposed strategy, whether each node distributes its estimation depends on an alternative parameter. The node only exchanges its estimation when the instant time meets some conditions. Next, each node combines the estimations of neighbors with its own estimation using combination coefficients upon the topology of the network. At last, the nodes update their estimations with a normalized LMS algorithm. The proposed AD-LMS strategy is compared to standard diffusion strategy. The results show that they achieve exactly the same coverage rate and nearly the network performance (network MSD and steady-state MSD) of standard diffusion strategy while reducing the communication burden significantly.

Citation


Lin Li and Donghui Li, "An Alternation Diffusion LMS Estimation Strategy Over Wireless Sensor Network," Progress In Electromagnetics Research M, Vol. 70, 135-143, 2018.
doi:10.2528/PIERM18042302
http://www.jpier.org/PIERM/pier.php?paper=18042302

References


    1. Rahman, M. U., "Performance analysis of MUSIC DOA algorithm estimation in multipath environment for automotive radars," International Journal of Applied Science & Engineering, Vol. 14, 125-132, 2016.

    2. Abdolee, R. and B. Champagne, "Diffusion LMS strategies in sensor networks with noisy input data," IEEE/ACM Transactions on Networking, Vol. 24, 3-14, 2015.
    doi:10.1109/TNET.2014.2350013

    3. Lopes, C. G. and A. H. Sayed, "Incremental adaptive strategies over distributed networks," IEEE Transactions on Signal Processing, Vol. 55, 4064-4077, 2007.
    doi:10.1109/TSP.2007.896034

    4. Liu, Y., C. Li, W. K. S. Tang, and Z. Zhang, "Distributed estimation over complex networks," Information Sciences, Vol. 197, 91-104, 2012.
    doi:10.1016/j.ins.2012.02.008

    5. Arablouei, R., Y. F. Huang, S. Werner, and K. Dogancay, "Reduced-communication diffusion LMS strategy for adaptive distributed estimation," Signal Processing, Vol. 117, 355-361, 2014.
    doi:10.1016/j.sigpro.2015.06.006

    6. Sahoo, U. K., G. Panda, B. Mulgrew, and B. Majhi, "Robust incremental adaptive strategies for distributed networks to handle outliers in both input and desired data," Signal Processing, Vol. 96, 300-309, 2014.
    doi:10.1016/j.sigpro.2013.09.006

    7. Cattivelli, F. S. and A. H. Sayed, "Analysis of spatial and incremental LMS processing for distributed estimation," IEEE Transactions on Signal Processing, Vol. 59, 1465-1480, 2011.
    doi:10.1109/TSP.2010.2100386

    8. Lopes, C. G. and A. H. Sayed, "Diffusion least-mean squares over adaptive networks: Formulation and performance analysis," IEEE Transactions on Signal Processing, Vol. 56, 3122-3136, 2008.
    doi:10.1109/TSP.2008.917383

    9. Cattivelli, F. S. and A. H. Sayed, "Diffusion LMS strategies for distributed estimation," IEEE Transactions on Signal Processing, Vol. 58, 1035-1048, 2010.
    doi:10.1109/TSP.2009.2033729

    10. Chen, J. and A. H. Sayed, Diffusion Adaptation Strategies for Distributed Optimization and Learning over Networks, IEEE Press, 2012.

    11. Fernandezbes, J., J. A. Azpicuetaruiz, M. T. M. Silva, and J. Arenasgarcia, "A novel scheme for diffusion networks with least-squares adaptive combiners,", Vol. 248, 1-6, 2012.

    12. Tewari, M. and K. S. Vaisla, "Performance study of SEP and DEC hierarchical clustering algorithm for heterogeneous WSN," 2014 6th International Conference on Computational Intelligence and Communication Networks, 385-389, Bhopal, India, 2014.

    13. Senouci, M. R., A.Mellouk, H. Senouci, and A. Aissani, "Performance evaluation of network lifetime spatial-temporal distribution for WSN routing protocols," Journal of Network and Computer Applications, Vol. 35, 1317-1328, Jul. 2012.
    doi:10.1016/j.jnca.2012.01.016

    14. Shao, X., F. Chen, Q. Ye, and S. Duan, "A robust diffusion estimation algorithm with self-adjusting step-size in WSNs," Sensors, Vol. 17, 824, 2017.
    doi:10.3390/s17040824

    15. Chen, F. and X. Shao, "Broken-motifs diffusion LMS algorithm for reducing communication load," Signal Processing, Vol. 133, 213-218, Apr. 1, 2017.
    doi:10.1016/j.sigpro.2016.11.005

    16. Yim, S. H., H. S. Lee, and W. J. Song, "A Proportionate diffusion LMS algorithm for sparse distributed estimation," IEEE Transactions on Circuits & Systems II Express Briefs, Vol. 62, 992-996, 2015.
    doi:10.1109/TCSII.2015.2435631

    17. Chen, Y., Y. Gu, and A. O. Hero, "Sparse LMS for system identification," IEEE International Conference on Acoustics, Speech and Signal Processing, 3125-3128, 2009.

    18. Xie, L., D. H. Choi, S. Kar, and H. V. Poor, "Fully distributed state estimation for wide-area monitoring systems," IEEE Transactions on Smart Grid, Vol. 3, 1154-1169, 2012.
    doi:10.1109/TSG.2012.2197764

    19. Di Lorenzo, P., S. Barbarossa, and A. H. Sayed, "Distributed spectrum estimation for small cell networks based on sparse diffusion adaptation," IEEE Signal Processing Letters, Vol. 20, 1261-1265, 2013.
    doi:10.1109/LSP.2013.2287373

    20. Sayin, M. O. and S. S. Kozat, "Compressive diffusion strategies over distributed networks for reduced communication load," IEEE Transactions on Signal Processing, Vol. 62, 5308-5323, 2014.
    doi:10.1109/TSP.2014.2347917

    21. Cheng, J., D. Yu, and Y. Yang, "A fault diagnosis approach for roller bearings based on EMD method and AR model," Journal of Vibration Engineering, Vol. 20, 350-362, 2006.