| Time synchronization with high precision and the low energy consumption is the key to effective data transmission in real-time communication networks.How to improve the accuracy of network synchronization while reducing the loss of resources and energy caused by node synchronization process in the complex network environment with the demand for diversified communication equipment and massive data transmission is the difficulty and hotspot of TSN time synchronization algorithm research.At the same time,as a distributed communication network model,the TSN network structure of a main wired network and wireless subnet is suitable for real-time data transmission of a cyber-physical system.In this paper,the synchronization accuracy of TSN time synchronization is poor,which is caused by clock frequency drift and random synchronization error.In order to improve the accuracy of network synchronization and reduce the energy consumption of network synchronization,a high-precision time synchronization algorithm based on the Kalman filter and a TSN time synchronization algorithm with low energy consumption is proposed,respectively.The main work of this paper is as follows:(1)Aiming at the problems such as poor precision of time synchronization between devices and large delay of information transmission in the industrial control network,a time synchronization optimization algorithm based on the improved KF for the time-sensitive network is studied.The clock frequency factor is introduced to establish an accurate time deviation mechanism,which can reduce the synchronization error caused by clock drift and solve the clock deviation of slave nodes.The Kalman adaptive neural filtering process was built to improve the time synchronization accuracy and reduce the persistence error.The multi-dimensional comparison between the simulation experiment and the traditional synchronization method shows that the algorithm proposed in this paper improves the synchronization accuracy of 100ns in the network level greater than 3 layers,and the stability of the algorithm is doubled after 50 rounds of synchronization.Simulation results show that the proposed algorithm can effectively improve the accuracy and stability of time synchronization between network equipment nodes.(2)Aiming at the problem of network energy consumption caused by TSN time synchronization process,a low energy consumption time synchronization optimization algorithm was proposed.The network is divided into wired main network and wireless subnet,and multi-sink nodes are established in the subnet to optimize routing structure A periodic dynamic optimization algorithm is proposed for the wired main network.For the multi-sink node network,the hierarchical synchronization algorithm was selected,and the parameter estimation optimization algorithm and the single-dual-direction hybrid synchronization algorithm were adopted for the primary and secondary networks to complete the time synchronization.In the simulation experiment,under the control of 1?s synchronization precision,the loss of synchronization resources can be reduced by 9.2%by sacrificing the synchronization precision.In the wireless subnet,the energy efficiency of the time synchronization algorithm is improved by nearly 15%when 100 nodes are added.The results show that the algorithm not only has high synchronization accuracy,but also reduces the network energy consumption in routing and synchronization.(3)Experimental verification of accuracy and energy consumption of TSN time synchronization algorithm.Two FET1028 development boards and 20 CC2530 sensor nodes were selected to carry out TSN time synchronization network model.The synchronization precision and synchronous energy consumption parameters were measured in the laboratory and playground environment respectively.The improved algorithm was carried out on the wired main network and the wireless subnetwork for comparative experimental verification.The results show that the improved algorithm is feasible in a real-time communication network,and shows good performance in the synchronization effect.Figure[42]table[5]reference[68]... |
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