Open Access

ARTICLE

Efficient One-Way Time Synchronization for VANET with MLE-Based Multi-Stage Update

Hyeontae Joo, Sangmin Lee, Kiseok Kim, Hwangnam Kim^*

School of Electrical Engineering, Korea University, Seoul, 02841, Republic of Korea

* Corresponding Author: Hwangnam Kim. Email:

(This article belongs to the Special Issue: Advanced Trends in Vehicular Ad hoc Networks (VANETs))

Computers, Materials & Continua 2025, 84(2), 2789-2804. https://doi.org/10.32604/cmc.2025.066304

Received 04 April 2025; Accepted 19 May 2025; Issue published 03 July 2025

Abstract

As vehicular networks become increasingly pervasive, enhancing connectivity and reliability has emerged as a critical objective. Among the enabling technologies for advanced wireless communication, particularly those targeting low latency and high reliability, time synchronization is critical, especially in vehicular networks. However, due to the inherent mobility of vehicular environments, consistently exchanging synchronization packets with a fixed base station or access point is challenging. This issue is further exacerbated in signal shadowed areas such as urban canyons, tunnels, or large-scale indoor halls where other technologies, such as global navigation satellite system (GNSS), are unavailable. One-way synchronization techniques offer a feasible approach under such transient connectivity conditions. One-way schemes still suffer from long convergence times to reach the required synchronization accuracy in these circumstances. In this paper, we propose a WLAN-based multi-stage clock synchronization scheme (WMC) tailored for vehicular networks. The proposed method comprises an initial hard update stage to rapidly achieve synchronization, followed by a high-precision stable stage based on Maximum Likelihood Estimation (MLE). By implementing the scheme directly at the network driver, we address key limitations of hard update mechanisms. Our approach significantly reduces the initial period to collect high-quality samples and offset estimation time to reach sub-50 s accuracy, and subsequently transitions to a refined MLE-based synchronization stage, achieving stable accuracy at approximately 30 s. The windowed moving average stabilized (reaching 90% of the baseline) in approximately 35 s, which corresponds to just 5.1% of the baseline time accuracy. Finally, the impact of synchronization performance on the localization model was validated using the Simulation of Urban Mobility (SUMO). The results demonstrate that more accurate conditions for position estimation can be supported, with an improvement about 38.5% in the mean error.

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