Intelligent and Low Overhead Network Synchronization for Large-Scale Industrial IoT Systems in the 6G Era

Abstract

Holistic temporal coherence among distributed industrial infrastructures enabled by accurate network synchronization is essential for achieving tight orchestration of large-scale industrial Internet of Things (IIoT) systems. However, the low efficiency and situation agnosticism of conventional synchronization techniques using an inflexible "observing-andcalibrating" approach for clock offset correction are inevitably hindering the performance of many IIoT applications with increasing scale and heterogeneity. In this article, we provide an in-depth analysis of the challenges associated with conventional synchronization schemes over large-scale IIoT systems and then present three promising research directions for achieving intelligent and low overhead IIoT synchronization. Particularly, we first propose a new modelbased network synchronization scheme that can proactively enable low overhead clock calibration by leveraging the inherent characteristics of heterogeneous clocks to avoid frequent timestamp exchange. We then design an intelligent device clustering mechanism with a specifically designed synchronization strategy for each group of IIoT devices by jointly exploiting their distinctive synchronization requirements and multi-dimensional device attributes. To leverage the unique characteristics of each oscillator, we analyze historical timestamps to reject unreliable timestamps and identify unreliable devices. Finally, we envision an edge-cloud collaborative network synchronization paradigm to implement the proposed schemes and demonstrate their efficacy in large-scale IIoT systems.