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Distributed network time synchronization: Social learning versus consensus
Hulede, Ian Ellis L.
Hulede, Ian Ellis L.
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dissertation
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2022-07
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Dissertation
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This dissertation proposes a social learning-based distributed network time synchronization
(SLDNTS) and compares it to a classic approach: consensus-based distributed
network time synchronization (CDNTS) under a Global Positioning System (GPS) denial
environment. An observation random variable (ORV), which is a conditional likelihood (e.g.,
Gaussian) given a synchronized true time hypothesis is used to generate clock times at each
node and each iteration. This proposal will then show a simple method to construct an
observation matrix that satisfies both the identifiability condition (IC) and the prevailing
observation signal existence condition (POSEC) required for the social learning (SL). Practical
clock parameters such as time offsets, frequency offsets, phase offsets, and observation
noises are referred to the International Telecommunication Union (ITU) standard and considered
in the evaluation. Then, this dissertation verifies, through simulation and the Allan
deviation criteria to show that the proposed SLDNTS shows superior performance compared
to the classic CDNTS under the same observation environment.
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Thesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of Electrical and Computer Engineering
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Wichita State University
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© Copyright 2022 by Ian Ellis Lamptey Hulede
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