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<p>In this paper, we construct a GCCP multivariate datalink network for teaching and learning resource sharing through the Generalized Control Chaining Primary Protocol (GCCP) combined with mutual cooperative services among the physical, data link and network layers for resource sharing. The solution set is mapped to the distributed dynamic self-organizing network data links through Nash equilibrium. Attribute descriptions for the necessary digital teaching resources are provided in a P2P system for sharing resources in large-scale heterogeneous teaching environments. Finally, simulations are conducted under the Group Service Control Protocol (GCCP) for teaching resource sharing to verify the effectiveness of the scheduling and management model for digital teaching resources. According to the results, the system with a multivariate datalink network maintains an average cluster size of 5 nodes. The response time of the system increases significantly with the increase of scale when RP(k)C-Grid takes k as 10,20,30,40,50 respectively. This paper effectively manages and maintains the entire digital resource management system for teaching and learning in universities, making it more stable and efficient.</p>
</abstract>

In this paper, we construct a GCCP multivariate datalink network for teaching and learning resource sharing through the Generalized Control Chaining Primary Protocol (GCCP) combined with mutual cooperative services among the physical, data link and network layers for resource sharing. The solution set is mapped to the distributed dynamic self-organizing network data links through Nash equilibrium. Attribute descriptions for the necessary digital teaching resources are provided in a P2P system for sharing resources in large-scale heterogeneous teaching environments. Finally, simulations are conducted under the Group Service Control Protocol (GCCP) for teaching resource sharing to verify the effectiveness of the scheduling and management model for digital teaching resources. According to the results, the system with a multivariate datalink network maintains an average cluster size of 5 nodes. The response time of the system increases significantly with the increase of scale when RP(k)C-Grid takes k as 10,20,30,40,50 respectively. This paper effectively manages and maintains the entire digital resource management system for teaching and learning in universities, making it more stable and efficient.

Digital Resource Management and Sharing for Teaching and Learning in Higher Education by Integrating Multiple Data Chain Networks

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, we construct a GCCP multivariate datalink network for teaching and learning resource sharing through the Generalized Control Chaining Primary...

Digital Resource Management and Sharing for Teaching and Learning in Higher Education by Integrating Multiple Data Chain Networks

Online veröffentlicht: 20. Nov. 2023

Seitenbereich: -

Eingereicht: 19. Jan. 2023

Akzeptiert: 31. Mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.01201

Schlüsselwörter
Multivariate data chains, Self-organizing networks, Nash equilibrium, Digital teaching resources, Control protocols

© 2023 Xiaojing Yan, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Digital Resource Management and Sharing for Teaching and Learning in Higher Education by Integrating Multiple Data Chain Networks

Online veröffentlicht: 20. Nov. 2023

Seitenbereich: -

Eingereicht: 19. Jan. 2023

Akzeptiert: 31. Mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.01201

SchlüsselwörterMultivariate data chains, Self-organizing networks, Nash equilibrium, Digital teaching resources, Control protocols

© 2023 Xiaojing Yan, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Multivariate data chains, Self-organizing networks, Nash equilibrium, Digital teaching resources, Control protocols