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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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A Dynamic QoS Mapping Algorithm for 5G-TSN Converged Networks Based on Weighted Fuzzy C-Means and Three-Way Decision

Yuhang Wu1, Fangmin Xu1, Lina Ning2

  • 1School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a dynamic weighted Quality of Service (QoS) mapping method for 5G-Time-Sensitive Networking (TSN) networks. The novel approach ensures consistent QoS and improves load balancing in converged networks.

Keywords:
5Gflow clusteringload balancingquality of service (QoS) mappingthree-way decisionstime-sensitive networking (TSN)

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Area of Science:

  • * Network Engineering
  • * Telecommunications

Background:

  • * Ensuring end-to-end Quality of Service (QoS) in converged 5G-Time-Sensitive Networking (TSN) environments presents significant challenges.
  • * Existing methods may struggle with dynamic network loads and complex QoS attribute management.

Purpose of the Study:

  • * To propose a novel dynamic weighted QoS mapping method for 5G-TSN converged networks.
  • * To enhance end-to-end QoS consistency and optimize load balancing.
  • * To develop an adaptive system capable of handling varying network conditions.

Main Methods:

  • * Utilized Weighted Fuzzy C-Means (WFCM) for clustering Time-Sensitive Networking (TSN) flows based on QoS attributes, reducing computational complexity.
  • * Employed a three-way decision-based method for assigning appropriate 5G QoS Identifier (5QI) values to TSN flow clusters.
  • * Implemented dynamic weight adjustments considering QoS similarity and residual load rate for adaptive network management.

Main Results:

  • * The proposed WFCM-TDwQM method demonstrated superior end-to-end QoS consistency compared to other mapping algorithm combinations.
  • * Achieved improved load balancing performance under diverse and varying network loads.
  • * Evaluated and confirmed effective mapping performance across different network scenarios.

Conclusions:

  • * The WFCM-TDwQM method effectively addresses the challenge of QoS management in 5G-TSN converged networks.
  • * The dynamic weighting mechanism allows for robust adaptation to network load fluctuations.
  • * The approach provides a promising solution for maintaining QoS and load balance in next-generation networks.