Internet of everything meets the metaverse: Bridging physical and virtual worlds with blockchain
Bibliographic record
Abstract
The Metaverse is an evolving technology that leverages the Internet infrastructure and the massively connected Internet of Everything (IoE) to create an immersive virtual world. In the Metaverse, humans engage in activities similar to those in the real world, such as socializing, working, attending events, exploring virtual landscapes, creating and trading digital assets, participating in virtual economies, and experiencing entertainment and cultural activities. By using advanced technologies such as IoE, extended reality (XR), artificial intelligence (AI), machine learning (ML), and 6G communication, along with blockchain technology, the Metaverse bridges the physical and virtual worlds. In particular, Blockchain-Enabled IoE (BIoE) will play a crucial role in Metaverse applications by ensuring secure service provisioning through the integration of blockchain with IoE. It efficiently manages the massive connectivity of physical world objects and enhances security, integrity, and decentralization of trust, while increasing resilience against failures, thus securing and fostering trust in both physical and virtual Metaverse networks. While some view the Metaverse as a detached virtual world, technologies like mixed reality and digital twins highlight the need for complementarity and interactive co-existence between the physical and virtual worlds. Blockchain facilitates this co-existence by providing a secure and trusted framework for integrating and synchronizing data and activities across both environments, developing trust through the reliability and authenticity of interactions and transactions. However, despite substantial advancements in related fields, there remains a significant gap in comprehensive surveys that address the integration of AI/ML, 6G, and blockchain in the Metaverse. In this paper, we fill this gap by examining BIoE’s capabilities in bridging the physical and virtual worlds and securing Metaverse applications across various domains, such as immersive energy grids, immersive healthcare, and immersive living. We explore BIoE’s role in service provisioning in the Metaverse, including access control, privacy protection, authentication, attack identification, and trust management. Additionally, we present a detailed taxonomy of existing literature, discuss novel use cases, and explore the synergies and practical implementations of BIoE in Metaverse applications. Finally, we address current challenges and propose future research directions to advance the field of BIoE in the Metaverse.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".