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    Distributed estimation based on observations prediction in wireless sensor networks

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    Type
    Article
    Authors
    Bouchoucha, Taha cc
    Ahmed, Mohammed F A
    Al-Naffouri, Tareq Y. cc
    Alouini, Mohamed-Slim cc
    KAUST Department
    Communication Theory Lab
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Date
    2015-03-11
    Online Publication Date
    2015-03-11
    Print Publication Date
    2015-10
    Permanent link to this record
    http://hdl.handle.net/10754/564200
    
    Metadata
    Show full item record
    Abstract
    We consider wireless sensor networks (WSNs) used for distributed estimation of unknown parameters. Due to the limited bandwidth, sensor nodes quantize their noisy observations before transmission to a fusion center (FC) for the estimation process. In this letter, the correlation between observations is exploited to reduce the mean-square error (MSE) of the distributed estimation. Specifically, sensor nodes generate local predictions of their observations and then transmit the quantized prediction errors (innovations) to the FC rather than the quantized observations. The analytic and numerical results show that transmitting the innovations rather than the observations mitigates the effect of quantization noise and hence reduces the MSE. © 2015 IEEE.
    Citation
    Bouchoucha, T., Ahmed, M. F. A., Al-Naffouri, T. Y., & Alouini, M.-S. (2015). Distributed Estimation Based on Observations Prediction in Wireless Sensor Networks. IEEE Signal Processing Letters, 22(10), 1530–1533. doi:10.1109/lsp.2015.2411852
    Sponsors
    This work was supported by the King Abdulaziz City of Science and Technology (KACST) under Grant AT-34-345. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Vincenzo Matta.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Signal Processing Letters
    DOI
    10.1109/LSP.2015.2411852
    ae974a485f413a2113503eed53cd6c53
    10.1109/LSP.2015.2411852
    Scopus Count
    Collections
    Articles; Electrical and Computer Engineering Program; Communication Theory Lab; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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