Exploring Secure Visible Light Communication in Next-generation (6G) Internet-of-Things
Bibliographic record
Abstract
This article presents a comprehensive survey of visible light communication (VLC) between devices in 6G internet of things (IoT) architecture. For effective stationary and mobile device-to-device communication in both indoors and outdoors, VLC is envisaged as a technique that can enable a robust and inexpensive, interference and radiation-free IoT communications. Whereas the demands on the growth in IoT network traffic and expanded verticals are met through 5G, 5G+ and beyond 5G (B5G); communication between two IoT devices in close vicinity without resorting to radio frequency (RF) spectrum usage is still a challenging problem and lies at a crucial research stage. One potential solution is to resort to optical wireless communication (OWC), especially VLC to venture into alternatives to radio frequency (RF) communication. In this article, we aim to bridge the gap between VLC and its applications in IoT through a comprehensive survey of VLC and its applications in IoT. We begin with an introduction to IoT and emerging verticals such as internet-of-metasurfaces, internet-of- reflecting-surfaces, internet-of -nanothings, internet-of-bionanomaterials, and internet-of-space-things. Based on the current survey, several recommendations for further research are discussed at the end of this article.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".