A Factor Graph Approach to Clock Offset Estimation in Wireless Sensor Networks
Aitzaz Ahmad, Davide Zennaro, Erchin Serpedin, Lorenzo Vangelista

TL;DR
This paper introduces a novel factor graph and Bayesian approach for estimating clock offsets in wireless sensor networks, accommodating different likelihood distributions and node oscillator imperfections, with theoretical bounds and extensive simulations.
Contribution
It presents a new convex optimization method for maximum likelihood estimation and a Bayesian factor graph approach for dynamic clock offset estimation, improving accuracy and robustness.
Findings
Proposed estimators perform close to theoretical bounds.
Bayesian approach effectively models oscillator imperfections.
Simulation results validate the estimators' accuracy across scenarios.
Abstract
The problem of clock offset estimation in a two way timing message exchange regime is considered when the likelihood function of the observation time stamps is Gaussian, exponential or log-normally distributed. A parametrized solution to the maximum likelihood (ML) estimation of clock offset, based on convex optimization, is presented, which differs from the earlier approaches where the likelihood function is maximized graphically. In order to capture the imperfections in node oscillators, which may render a time-varying nature to the clock offset, a novel Bayesian approach to the clock offset estimation is proposed by using a factor graph representation of the posterior density. Message passing using the max-product algorithm yields a closed form expression for the Bayesian inference problem. Several lower bounds on the variance of an estimator are derived for arbitrary exponential…
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Taxonomy
TopicsNetwork Time Synchronization Technologies · Energy Efficient Wireless Sensor Networks · Advanced Memory and Neural Computing
