Blockchain-Based Semantic Information Sharing and Pricing for Web 3.0
Web 3.0 is a promising next-generation Internet paradigm that is driven by multiple technologies, e.g., semantic communications, besides blockchain to enable transparent and traceable data sharing. Specifically, semantic communication can provide Web 3.0 services with a promising and lightweight par...
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Published in | IEEE transactions on network science and engineering Vol. 11; no. 5; pp. 3918 - 3930 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Piscataway
IEEE
01.09.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | Web 3.0 is a promising next-generation Internet paradigm that is driven by multiple technologies, e.g., semantic communications, besides blockchain to enable transparent and traceable data sharing. Specifically, semantic communication can provide Web 3.0 services with a promising and lightweight paradigm for semantic information transmission. However, a unified integration framework has not been studied. Moreover, how to achieve efficient semantic sharing and pricing challenges the traditional centralized communication paradigm. Therefore, this paper integrates blockchain and semantic communication to share valuable semantic information for Web 3.0 services. The Proof of Semantic mechanism is designed to reduce the garbage-in garbage-out challenge of Web 3.0 services. Moreover, we utilize the state channel and the information bottleneck to construct a task-relevant semantic sharing mechanism to exchange information efficiently in Web 3.0 services. To maximize semantic information's utility, we propose a semantic pricing mechanism based multi-leader multi-follower Stackelberg game to circulate information in Web 3.0 services. Simulation results indicate that the proposed mechanisms are both verifiable and efficient, achieving reduced communication overheads while maintaining test accuracy comparable to benchmark methods. |
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ISSN: | 2327-4697 2334-329X |
DOI: | 10.1109/TNSE.2023.3345335 |