Performance Evaluation of Edge Computing-Aided IoT Augmented Reality Systems
Bibliographic record
Abstract
Envisioned mobile augmented reality (MAR) ushers a new plethora of smart applications. However, the resource-constrained nature of head-mounted devices (HMDs) has limited the development of MAR systems. In this regard, edge computing has emerged as a promising solution for the processing of MAR computer-intensive tasks. In edge-aided MAR systems, HMDs will offload to edge nodes part of the acquired video frames, which reduces the latency and energy cost for video analytics in MAR systems. In this paper, we devise a queuing theory-based mathematical framework for guiding the design of MAR systems. The proposed mathematical framework models the characteristics of HMDs and edge devices, and the different network conditions. It serves as a tool for directing the decision-making in the design of MAR systems under different conditions and applications. Extensive numerical evaluations show that offloading frames to edge servers at a proper rate can significantly reduce the total average latency while a higher offloading rate incurs lower energy costs at MAR devices.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".