IEEE 1588 for Clock Synchronization in Industrial IoT and Related Applications: A Review on Contributing Technologies, Protocols and Enhancement Methodologies

Idrees, Zeba; Granados, J. M. del Castillo; Sun, Yang; Latif, Shahid; Gong, Li; Zou, Zhuo; Zheng, Lirong

doi:10.1109/access.2020.3013669

Cited by 37 publications

(15 citation statements)

References 109 publications

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“…This will enhance the learning process for diverse industrial devices, resulting in an intelligent IIoT network with reduced computing complexity and increased network lifetime. The best example is the employment of a hardware-in-the-loop (HIL) simulation platform [172][173][174][175][176]. The HIL combines computations for numerous processes with the internal hardware of sensors and actuators.…”

Section: Lightweight Learning Frameworkmentioning

confidence: 99%

Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions

Latif

Driss

Boulila

et al. 2021

Sensors

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The Industrial Internet of Things (IIoT) refers to the use of smart sensors, actuators, fast communication protocols, and efficient cybersecurity mechanisms to improve industrial processes and applications. In large industrial networks, smart devices generate large amounts of data, and thus IIoT frameworks require intelligent, robust techniques for big data analysis. Artificial intelligence (AI) and deep learning (DL) techniques produce promising results in IIoT networks due to their intelligent learning and processing capabilities. This survey article assesses the potential of DL in IIoT applications and presents a brief architecture of IIoT with key enabling technologies. Several well-known DL algorithms are then discussed along with their theoretical backgrounds and several software and hardware frameworks for DL implementations. Potential deployments of DL techniques in IIoT applications are briefly discussed. Finally, this survey highlights significant challenges and future directions for future research endeavors.

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Section: Lightweight Learning Frameworkmentioning

confidence: 99%

Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions

Latif

Driss

Boulila

et al. 2021

Sensors

Self Cite

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“…When multiple slaves are present in a network, peer delay packets are only sent to the nearest node, not all the way upstream to the master. Lastly, if the network is suddenly changed due to a fault or similar, recovery time is reduced as not all delay calculations will be affected [22].…”

Section: White Rabbit Precision Time Protocol (Wr-ptp)mentioning

confidence: 99%

Enhancing White Rabbit Synchronization Stability and Scalability Using P2P Transparent and Hybrid Clocks

Gutierrez-Rivas

Torres-Gonzalez

Ros

et al. 2021

IEEE Trans. Ind. Inf.

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Time synchronization faces an increasing performance demand nowadays. This is particularly common in different segments, such as new scientific infrastructures, power grid, telecommunications or 5th-Generation Wireless Networks (5G). All of them share the need for a time synchronization technology offering a very low synchronization error along large networks. The new version of the IEEE-1588-2019 protocol offers a considerable performance improvement thanks to the High Accuracy profile, based on White Rabbit (WR). The original WR implementation guarantees a synchronization accuracy below one nanosecond for a small number of cascaded nodes. This contribution evaluates the performance degradation when a considerable number of devices are deployed in cascaded configurations. In order to improve the performance, different delay measurement implementations have been evaluated in a series of experiments. The results prove the considerable benefits of our implementation against the current WR implementation.

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“…The nodes in the network use their local clocks to determine the position of the time slot. Even if these local clocks use crystal oscillators with the same frequency, the time error of the crystal oscillator will occur due to the instability of their own operating state and the influence of external environmental factors, which will also cause a time offset between their local clocks [2]. Therefore, a clock synchronization mechanism is necessary to make the time of each node the same.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Method for Nanosecond Time Synchronization in IEEE 1588 Precision Time Protocol

Liu

Qi³

et al. 2023

Processes

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The performance of time-critical systems depends heavily on time synchronization accuracy. Therefore, it is crucial to have a synchronization method that can achieve high time synchronization accuracy. In this paper, we propose a new underlying transmission architecture and new synchronization messages. On the basis of these, aiming at the time error problem of the slave clock, we propose an enhanced time synchronization method based on new synchronization messages. Furthermore, we evaluate the performance of the enhanced time synchronization method on the OMNeT++ simulator. In addition, we compare the impact of different crystal oscillator accuracies and different crystal oscillator frequencies on time synchronization accuracy, respectively. Simulation results show that the time offset is at most ±1 clock period using the enhanced time synchronization method. We realize the purpose of timing the master clock and the slave clock by counting the period of the clock signal. Therefore, we needed to round down the time count to an integer. This is the reason why −1 and 1 appear at the same time. When the crystal oscillator frequency used is 80 MHz, the system can achieve a time synchronization accuracy of ±12.5 ns; that is, a nanosecond-level time synchronization accuracy can be achieved. With the reduction of the crystal oscillator accuracy of the slave clock, the synchronization accuracy of ±1 clock period can still be achieved. With the increase in the crystal oscillator frequency, the time synchronization accuracy that can be achieved also improves. The method proposed in this paper provides a new way of thinking and has certain guiding significance for improving the time synchronization accuracy of time-critical systems.

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IEEE 1588 for Clock Synchronization in Industrial IoT and Related Applications: A Review on Contributing Technologies, Protocols and Enhancement Methodologies

Cited by 37 publications

References 109 publications

Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions

Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions

Enhancing White Rabbit Synchronization Stability and Scalability Using P2P Transparent and Hybrid Clocks

An Enhanced Method for Nanosecond Time Synchronization in IEEE 1588 Precision Time Protocol

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