Incentive Mechanism for Data Sharing in Smart Manufacturing Under the Industrial Internet
This study proposes an incentive mechanism for data sharing in smart manufacturing under the Industrial Internet. The mechanism addresses several key challenges in data sharing, including data silos among manufacturing devices, insufficient incentive structures, dynamic supply-demand imbalances unde...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11025542/ |
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| Summary: | This study proposes an incentive mechanism for data sharing in smart manufacturing under the Industrial Internet. The mechanism addresses several key challenges in data sharing, including data silos among manufacturing devices, insufficient incentive structures, dynamic supply-demand imbalances under production task and equipment state constraints, and the high computational complexity of achieving global equilibrium in large-scale distributed systems. Grounded in Walrasian equilibrium theory, this mechanism models device holders and the Industrial Internet Platform as participants in a competitive market. By leveraging price signals, it enables the adaptive matching of data supply from device holders with globally optimized demand, ensuring Pareto-optimal outcomes in terms of device holder revenue, production efficiency, and overall social welfare. Furthermore, numerical experiments are conducted to analyze the impact of key parameters on the optimal data pricing and data upload volume. The results demonstrate that this mechanism effectively maximizes the utility of the production system. The comparative experiments prove that this incentive mechanism has significant advantages in terms of optimal data price and social welfare. However, due to the heterogeneity and nonlinearity of actual utilities among device holders and the platform, further validation and refinement of the model using real-world transaction data are necessary. |
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| ISSN: | 2169-3536 |