Edge Caching and Computing in 5G for Mobile AR/VR and Tactile Internet
Bibliographic record
Abstract
As a result of increasing popularity of augmented reality and virtual reality (AR/VR) applications, there are significant efforts to bring AR/VR to mobile users. Parallel to the advances in AR/VR technologies, tactile internet is gaining interest from the research community. Both AR/VR and tactile internet applications require massive computational capability, high communication bandwidth, and ultra-low latency that cannot be provided with the current wireless mobile networks. By 2020, long term evolution (LTE) networks will start to be replaced by fifth generation (5G) networks. Edge caching and mobile edge computing are among the potential 5G technologies that bring content and computing resources close to the users, reducing latency and load on the backhaul. The aim of this survey is to present current state-of-the-art research on edge caching and computing with a focus on AR/VR applications and tactile internet and to discuss applications, opportunities and challenges in this emerging field.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 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".