Internet of everything meets the metaverse: Bridging physical and virtual worlds with blockchain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it