Distributed network time synchronization: Social learning versus consensus

Abstract

The 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.