IoT and Metaverse Integration: Frameworks and Future Applications
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I4P109Keywords:
Internet of Things (IoT), Metaverse, Digital Twin Technology, Cyber-Physical Systems, Immersive Virtual Environments, Semantic Interoperability, 6G and Edge Computing, Smart Cities, Real-Time Data Synchronization, IoT-Metaverse Integration FrameworksAbstract
The IoT-Metaverse Nexus constitutes one of the very few fields capable of inducing a paradigmatic shift in the manner physical and virtual environments co-opt each other into creating immersive, intelligent, and interconnected digital-physical systems. IoT, emphasizing networks of embedded sensors and devices, acts as a conduit for real-time data, whereas the Metaverse provides spatially enhanced, persistent virtual worlds for enabling embodied digital experiences. This integration of the two domains might lead to never-before-imagined applications in smart cities, digital healthcare, industrial automation, and immersive education. Nonetheless, serious challenges confront the integration, including latency handling, semantic interoperability, data synchronization, infrastructural scalability, and security concerns. The article examines the fundamental technologies and architectures that form the basis of integrating the Metaverse and IoT, proposing a layered integration framework involving perception, network, middleware, application, and immersive layers.
With digital twins, real-time synchronization becomes feasible and is maintained between physical assets and their virtual counterparts. The paper also presents a few emerging case studies in industrial and urban settings, outlining instances where immersive environments, enriched by real-world data, augment human decision-making and interaction. It further scrutinizes prospective avenues with the support of 6G networks, AI swift orchestration, and decentralized Web3 infrastructures for proposing scalable and secure IoT-Metaverse ecosystems. By laying out technical, ethical, and infrastructural concerns, this study seeks to frame a clearer picture of how these two fast-evolving paradigms can converge to reformulate digital interaction across many disciplines. The findings demonstrate a strong need to create an interdisciplinary research endeavor and standardization framework that will enable unleashed power of the dualized technological future
References
[1] Ali, M., Khan, S., & Alsaqer, M. (2022). Blockchain-based security frameworks for immersive environments. Computers & Security, 117, 102691.
[2] Bassi, A., Bauer, M., Fiedler, M., & Van der Meer, S. (2013). Enabling things to talk: Designing IoT solutions with the IoT architectural reference model. Springer.
[3] Bonino, D., & Corno, F. (2014). Review of the state-of-the-art in smart homes. Journal of Ambient Intelligence and Smart Environments, 6(2), 179–206.
[4] McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Aguilera, A. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data (FedAvg). arXiv:1602.05629.
[5] Chen, M., Ma, Y., Li, Y., Wu, D., Zhang, Y., & Youn, C. H. (2017). Wearable 2.0: Enabling human-cloud integration in next generation healthcare systems. IEEE Communications Magazine, 55(1), 54–61.
[6] Dionisio, J. D. N., Burns, W. G., & Gilbert, R. (2013). 3D virtual worlds and the metaverse: Current status and future possibilities. ACM Computing Surveys, 45(3), 34.
[7] Rautaray, S., & Tayagi, D. (2023). Artificial Intelligence in Telecommunications: Applications, Risks, and Governance in the 5G and Beyond Era. Artificial Intelligence
[8] Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016). Federated Learning: Strategies for Improving Communication Efficiency. arXiv:1610.05492.
[9] Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952–108971.
[10] Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660.
[11] Lin, Y., Han, S., Mao, H., Wang, Y., & Dally, W. J. (2018). Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training. ICLR.
[12] Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated Optimization in Heterogeneous Networks (FedProx). MLSys.
[13] Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243.
[14] Laxman doddipatla, & Sai Teja Sharma R.(2023). The Role of AI and Machine Learning in Strengthening Digital Wallet Security Against Fraud. Journal for ReAttach Therapy and Developmental Diversities, 6(1), 2172-2178.
[15] Lee, L. H., Braud, T., Zhou, P., Wang, L., Xu, D., Lin, Z., ... & Hui, P. (2021). All one needs to know about Metaverse: A complete survey on technological singularity, virtual ecosystem, and research agenda. arXiv preprint arXiv:2110.05352.
[16] Reddi, S. J., et al. (2021). Adaptive Federated Optimization (FedAdam, FedYogi, FedAdagrad). arXiv:2003.00295 / MLSys.
[17] D Alexander.(2022). EMERGING TRENDS IN FINTECH: HOW TECHNOLOGY IS RESHAPING THE GLOBAL FINANCIAL LANDSCAPE. Journal of Population Therapeutics and Clinical Pharmacology, 29(02), 573-580.
[18] RA Kodete. (2022). Enhancing Blockchain Payment Security with Federated Learning. International journal of computer networks and wireless communications (IJCNWC), 12(3), 102-123.
[19] Ning, H., Liu, H., Yang, L. T., & Chen, Z. (2021). A survey of the research status of digital twin and its application prospects. Expert Systems with Applications, 178, 114848.
[20] CT Aghaunor. (2023). From Data to Decisions: Harnessing AI and Analytics. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(3), 76-84. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I3P109
[21] Alistarh, D., Hoefler, T., Johansson, M., & Konstantinov, N. (2017/2018). TernGrad / QSGD & Sparse/Ternary Compression for Communication-Efficient Training. ICML/NeurIPS.
[22] Ribeiro-Navarrete, S., Saura, J. R., & Palacios-Marqués, D. (2021). Towards a new era of mass data collection: Assessing Metaverse privacy implications. Technological Forecasting and Social Change, 173, 121092.
[23] B Naticchia, “Unified Framework of Blockchain and AI for Business Intelligence in Modern Banking ”, IJERET, vol. 3, no. 4, pp. 32–42, Dec. 2022, doi: 10.63282/3050-922X.IJERET-V3I4P105
[24] Nguyen, M.-D., Lee, S.-M., Pham, Q.-V., Hoang, D. T., Nguyen, D. N., & Hwang, W.-J. (2022). HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large-Scale IoT Networks. arXiv:2204.06760.
[25] S Mishra, and A Jain, “Leveraging IoT-Driven Customer Intelligence for Adaptive Financial Services”, IJAIDSML, vol. 4, no. 3, pp. 60–71, Oct. 2023, doi: 10.63282/3050-9262.IJAIDSML-V4I3P107
[26] Sun, Y., Zhang, Y., & Chen, J. (2021). Challenges of integrating Metaverse with urban IoT infrastructure. Journal of Urban Technology, 28(4), 23–41.
[27] Tao, F., Zhang, M., Liu, Y., & Nee, A. Y. C. (2018). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415.
[28] R. R. Yerram, "Risk management in foreign exchange for crossborder payments:Strategies for minimizing exposure," Turkish Online Journal of Qualitative Inquiry, pp. 892-900, 2020.
[29] Wang, F. Y., & Zhang, Y. (2019). The future of intelligent cities: Digital twins and cognitive infrastructure. IEEE Systems Journal, 13(3), 3512–3520.
[30] Prakash, P., Ding, J., Chen, R., Qin, X., Shu, M., Cui, Q., Guo, Y., & Pan, M. (2022). IoT Device Friendly and Communication-Efficient Federated Learning via Joint Model Pruning and Quantization. IEEE Internet of Things Journal, 9(15), 13638–13650.
[31] T Anthony. (2021). AI Models for Real Time Risk Assessment in Decentralized Finance. Annals of Applied Sciences, 2(1). Retrieved from https://annalsofappliedsciences.com/index.php/aas/article/view/30
[32] Jiang, Y., Wang, S., Valls, V., Ko, B. J., Lee, W.-H., Leung, K. K., & Tassiulas, L. (2023). Model Pruning Enables Efficient Federated Learning on Edge Devices. IEEE TNNLS, 34(12), 10374–10386.
[33] AB Dorothy. GREEN FINTECH AND ITS INFLUENCE ON SUSTAINABLE FINANCIAL PRACTICES. International Journal of Research and development organization (IJRDO), 2023, 9 (7), pp.1-9. ⟨10.53555/bm.v9i7.6393⟩. ⟨hal-05215332⟩
[34] Hemalatha Naga Himabindu, Gurajada. (2022). Unlocking Insights: The Power of Data Science and AI in Data Visualization. International Journal of Computer Science and Information Technology Research (IJCSITR), 3(1), 154-179. https://doi.org/10.63530/IJCSITR_2022_03_01_016
[35] Hassan, W. U., Nawaz, M. T., Syed, T. H., Arfeen, M. I., Naseem, A., & Noor, S. (2015). Investigating Students' Behavioral Intention Towards Adoption of Mobile Learning in Higher Education Institutions of Pakistan. Technical Journal of University of Engineering & Technology Taxila, 20(3).
