A diffusion-based distributed EM algorithm for density estimation in wireless sensor networks
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessRestricted access - publisher's policy
We address the problem of distributed estimation of a parameter from a set of noisy observations collected by a sensor network, assuming that some sensors may be subject to data failures and report only noise. In such scenario, simple schemes such as the Best Linear Unbiased Estimator result in an error floor in moderate and high signal-to-noise ratio (SNR), whereas previously proposed methods based on hard decisions on data failure events degrade as the SNR decreases. Aiming at optimal performance within the whole range of SNRs, we adopt a Maximum Likelihood framework based on the Expectation-Maximization (EM) algorithm. The statistical model and the iterative nature of the EM method allow for a diffusion-based distributed implementation, whereby the information propagation is embedded in the iterative update of the parameters. Numerical examples show that the proposed algorithm practically attains the Cramer-Rao Lower Bound at all SNR values and compares favorably with other approaches.
CitationSilva, S.; López, R.; Pages, A. A diffusion-based distributed EM algorithm for density estimation in wireless sensor networks. A: IEEE International Conference on Acoustics, Speech, and Signal Processing. "2013 IEEE International Conference on Acoustics, Speech, and Signal Processing: proceedings: May 26-31, 2013: Vancouver Convention Center: Vancouver, British Columbia, Canada". Vancouver: Institute of Electrical and Electronics Engineers (IEEE), 2013, p. 4449-4453.
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