Blockchain-Based Integrated Sensing and Communication Services in 6G Networks
Bibliographic record
Abstract
Growth in the metaverse has been significant in recent years, aided by the robust communication capacities of the 5G network. Moving into the 6G era, integrated sensing and communication (ISAC) services are regarded as a potential solution for controlling avatars in the metaverse, because ISAC can enable users to have all-pervasive sensing capabilities at all times without needing additional equipment. However, current research primarily delves into the technological aspects of ISAC under the presumption of these sensing services being handled by centralized entities, such as corporations or organizations, while considerably less attention has been given to the potential issues inherent in such centralized control, including problems of privacy and the risks from monopolies. In light of these gaps, our paper introduces a novel framework for implementing blockchain-based ISAC services in 6G networks. Recognizing the associated challenges and possible risks with our proposed framework, we provide an interactive process featuring appropriate solutions. Moreover, this paper further evaluates the capacity and performance of our theoretical framework through a case simulation based on the real-world distribution of base stations. Furthermore, we conclude the limitations inherent within our proposed blockchain-based ISAC services and provide a list of relevant and consequential future research topics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".