The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.
Fusing Blockchain and AI With Metaverse: A Survey
Open accessAbstract
Metaverse as the latest buzzword has attracted great attention from both industry and academia. Metaverse seamlessly integrates the real world with the virtual world and allows avatars to carry out rich activities including creation, display, entertainment, social networking, and trading. Thus, it is promising to build an exciting digital world and to transform a better physical world through the exploration of the metaverse. In this survey, we dive into the metaverse by discussing how Blockchain and Artificial Intelligence (AI) fuse with it through investigating the state-of-the-art studies across the metaverse components, digital currencies, AI applications in the virtual world, and blockchain-empowered technologies. Further exploitation and interdisciplinary research on the fusion of AI and Blockchain towards metaverse will definitely require collaboration from both academia and industries. We wish that our survey can help researchers, engineers, and educators build an open, fair, and rational future metaverse.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972
Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2975749 · Cited by 6,210
Deep Reinforcement Learning: A Brief Survey
Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2743240 · Cited by 4,733
The future of digital health with federated learning
Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00323-1 · Cited by 4,015 · cited 1×
“With respect to the data privacy, there have been many research on applying FL in medical institutions [68], industries [66], banks [69], etc.”
BackgroundThe Future of Digital Health with Federated Learning
Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B., Maier-Hein, K., et al. (2020) · arXiv · doi:10.48550/arxiv.2003.08119 · Cited by 3,495
Digital twin and big data towards smart manufacturing and Industry 4.0: 360 degree comparison
Qi, Q., & Tao, F. (2018) · IEEE Access · doi:10.1109/access.2018.2793265 · Cited by 1,716
Blockchain Meets IoT: An Architecture for Scalable Access Management in IoT
Novo, O. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2812239 · Cited by 1,318
3D Virtual worlds and the metaverse: Current status and future possibilities
Dionisio, J. D. N., III, W. G. B., & Gilbert, R. (2013) · ACM Computing Surveys · doi:10.1145/2480741.2480751 · Cited by 1,151
Lee, L.-H., Braud, T., Zhou, P., Wang, L., Xu, D., Lin, Z., Kumar, A., Bermejo, C., & Hui, P. (2021) · arXiv · doi:10.48550/arxiv.2110.05352 · Cited by 1,088
Lee, L.-H., Braud, T., Zhou, P. Y., Wang, L., Xu, D., Lin, Z., Kumar, A., Bermejo, C., & Hui, P. (2024) · Foundations and Trends® in Human-Computer Interaction · doi:10.1561/1100000095 · Cited by 1,082
Cited by
Blockchain for the metaverse: A Review
Huynh-The, T., Gadekallu, T. R., Wang, W., Yenduri, G., Ranaweera, P., Pham, Q.-V., da Costa, D. B., & Liyanage, M. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.02.008 · Cited by 771
Habbal, A., Ali, M. K., & Abuzaraida, M. A. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122442 · Cited by 407
Blockchain Meets Metaverse and Digital Asset Management: A Comprehensive Survey
Truong, V. T., Le, L., & Niyato, D. (2023) · IEEE Access · doi:10.1109/access.2023.3257029 · Cited by 253
Toward immersive communications in 6G
Shen, X. (., Gao, J., Li, M., Zhou, C., Hu, S., He, M., & Zhuang, W. (2023) · Frontiers in Computer Science · doi:10.3389/fcomp.2022.1068478 · Cited by 124
Heterogeneous rarity patterns drive price dynamics in NFT collections
Mekacher, A., Bracci, A., Nadini, M., Martino, M., Alessandretti, L., Aiello, L. M., & Baronchelli, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-17922-5 · Cited by 110
Metadata
- Authors
- Yang, Q., Zhao, Y., Huang, H., Xiong, Z., Kang, J., & Zheng, Z.
- Venue
- IEEE Open Journal of the Computer Society
- Published
- 2022
- Volume
- 3
- Pages
- 122-136
- ISSN
- 2644-1268 (ISSN-L)
- OpenAlex
- W4226225860
- Cited by
- 554
- References
- 66
- FWCI
- 130.12
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Fusing Blockchain and AI With Metaverse: A Survey. https://app.cochat.ai/papers/10.1109/ojcs.2022.3188249
- The paper
- Yang, Q., Zhao, Y., Huang, H., Xiong, Z., Kang, J., & Zheng, Z. (2022). Fusing Blockchain and AI With Metaverse: A Survey. IEEE Open Journal of the Computer Society. https://doi.org/10.1109/ojcs.2022.3188249
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
