Cloud-MERVS: An IoT Cloud-Based Error Recovery Video Streaming Scheme
Bibliographic record
Abstract
Internet of Thing (IoT) cloud system is a field attracting a great deal of recent attention. Vehicular networks is a key area in IoT, as they can offer novel experiences in infotainment, as well as driving assistance such as like real-time traffic forecasting, advertisements and on board entertainment. Most of these applications rely on high quality video sharing, which is still an open research topic in Vehicular networks. We proposed a Cloud-MERVS technique for video streaming in Vehicular networks, which integrates IoT cloud system techniques and the Multi-channel Error Recovery Video Streaming (MERVS), to provide a more satisfying driving and travel experience. There are two types of service providers in cloud computing: Control Service Providers (CSPs) and Video Service Providers (VSPs). CSPs are responsible for collecting advertisement messages from the VSPs. The VSPs are the real video and service providers. Some VSPs are connected to a selected CSP to form a Group Service Provider (GSP), which is organized in a hierarchical structure for to improve efficiency. The design is implemented in Network Simulator-2 (NS-2). In this paper, several verification simulations are conducted, and the simulation results are presented.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.008 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".