The DOI matches its Crossref record; 172 of the 172 references it cites were found in a registry or an index.
When Internet of Things Meets Metaverse: Convergence of Physical and Cyber Worlds
Open accessAbstract
In recent years, the Internet of Things (IoT) has been studied in the context of the Metaverse to provide users with immersive cyber-virtual experiences in mixed-reality environments. This survey introduces six typical IoT applications in the Metaverse, including collaborative healthcare, education, smart city, entertainment, real estate, and socialization. In the IoT-inspired Metaverse, we also comprehensively survey four pillar technologies that enable augmented reality (AR) and virtual reality (VR), namely, responsible artificial intelligence (AI), high-speed data communications, cost-effective mobile edge computing (MEC), and digital twins. According to the physical-world demands, we outline the current industrial efforts and seven key requirements for building the IoT-inspired Metaverse: immersion, variety, economy, civility, interactivity, authenticity, and independence. In addition, this survey describes the open issues in the IoT-inspired Metaverse, which need to be addressed to eventually achieve the convergence of physical and cyber worlds.
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Cites
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00986 · Cited by 35,534
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., et al. (2019) · arXiv · doi:10.48550/arxiv.1910.10045 · Cited by 11,321
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
Federated Machine Learning: Concept and Applications
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322
Digital Twin in Industry: State-of-the-Art
Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2873186 · Cited by 4,391
Digital twin: Enabling technologies, challenges and open research
Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020) · IEEE Access · doi:10.1109/access.2020.2998358 · Cited by 2,965
Dwivedi, Y. K., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., Al-Debei, M. M., Dennehy, D., Metri, B., Buhalis, D., Cheung, C. M. K., et al. (2022) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2022.102542 · Cited by 2,753
Toward 6G Networks: Use Cases and Technologies
Giordani, M., Polese, M., Mezzavilla, M., Rangan, S., & Zorzi, M. (2020) · IEEE Communications Magazine · doi:10.1109/mcom.001.1900411 · Cited by 2,241
Mystakidis, S. (2022) · Encyclopedia · doi:10.3390/encyclopedia2010031 · Cited by 1,977
A Metaverse: Taxonomy, Components, Applications, and Open Challenges
Park, S.-M., & Kim, Y.-G. (2022) · IEEE Access · doi:10.1109/access.2021.3140175 · Cited by 1,824
Cited by
Wan, Y., Qu, Y., Ni, W., Xiang, Y., Gao, L., & Hossain, E. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3361451 · Cited by 152
“With the widespread use of AI applications and user/sensor data involved during the AI training process, the security and privacy issues have become critical [10].”
BackgroundSelf-citationData-Agnostic Model Poisoning Against Federated Learning: A Graph Autoencoder Approach
Li, K., Zheng, J., Yuan, X., Ni, W., Akan, O. B., & Poor, H. V. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3362147 · Cited by 41
Self-citationExploring Adversarial Graph Autoencoders to Manipulate Federated Learning in The Internet of Things
Li, K., Yuan, X., Zheng, J., Ni, W., & Guizani, M. (2023) · 2023 International Wireless Communications and Mobile Computing (IWCMC) · doi:10.1109/iwcmc58020.2023.10183217 · Cited by 8
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Metadata
- Authors
- Li, K., Cui, Y., Li, W., Lv, T., Yuan, X., Li, S., Ni, W., Simsek, M., & Dressler, F.
- Venue
- IEEE Internet of Things Journal
- Published
- 2023-03-01
- Volume
- 10
- Issue
- 5
- Pages
- 4148-4173
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W4313291084
- Cited by
- 266
- References
- 172
- FWCI
- 26.05
- Type
- journal article
- Language
- en
- Open access
- green
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- Li, K., Cui, Y., Li, W., Lv, T., Yuan, X., Li, S., Ni, W., Simsek, M., & Dressler, F. (2023). When Internet of Things Meets Metaverse: Convergence of Physical and Cyber Worlds. IEEE Internet of Things Journal. https://doi.org/10.1109/jiot.2022.3232845
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