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dc.contributor.advisorKwon, Hyuck M.
dc.contributor.authorHulede, Ian Ellis L.
dc.descriptionThesis (M.S.)-- Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science
dc.description.abstractThe objective of this thesis is to investigate social learning-based distributed network time synchronization (SLDNTS) and compare it to a classic approach: consensus DNTS (CDNTS). To achieve this objective, the thesis introduces a method for generating a practical observation random variable (ORV) for SLDNTS and presents both the worst and best ORV. Then, this thesis shows, through simulations, that SLDNTS is more robust than CDNTS in convergence. CDNTS fails when timing measurement error is nonzero, i.e.,a Gaussian random variable with mean equal to true time and variance equal to one is applied as an observation random variable (ORV) at each node. On the other hand SLDNTS shows quick convergence with small number of iterations even if nonzero timing measurement error, i.e., a Gaussian random variable, is applied.
dc.format.extentx, 30 pages
dc.publisherWichita State University
dc.rightsCopyright 2020 by Ian Ellis L. Hulede All Rights Reserved
dc.subject.lcshElectronic dissertations
dc.titleDistributed network time synchronization: Social learning versus consensus

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  • Master's Theses
    This collection includes Master's theses completed at the Wichita State University Graduate School (Fall 2005 --)

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