Mobility-Aware Proactive Edge Caching for Large Files in the Internet of Vehicles
Bibliographic record
Abstract
By shifting the requested content to the edge in the Internet of Vehicles (IoV), edge caching is expected to be an effective solution to satisfy the low latency and high-reliability requirements of IoV users for multimedia services. However, the edge node’s coverage area and storage space are limited. Moreover, since vehicles have high mobility and in-vehicle multimedia applications require sequential delivery for contents, we need to address two main issues: 1) how to optimize the proactive content caching decision (i.e., the placement of cached content chunks) among edge nodes (ENs) to provide better Quality of Services (QoS) for IoV users and 2) how to ensure that vehicles can download the required contents sequentially to improve Quality of Experience (QoE). In this article, we propose a mobility-aware proactive edge caching scheme (MSTPS), where the spatial and temporal prediction of vehicles are taken into account for content deployment and scheduling. Specifically, we optimize the caching decision based on predicting the vehicle’s driving trajectory and travel preference. The scheme learns the vehicle’s travel preferences to cope with mobility uncertainty by combining users with similar travel patterns. Meanwhile, the proposed scheme can support the sequential downloading of content chunks. Furthermore, in order to deal with the dynamic characteristics and unpredictable challenges of the IoV, we design a system recovery strategy, which can avoid the degradation of the proposed scheme due to the failure of prediction. Finally, by using real mobility data sets and scenarios, we explore the impact of the number of ENs deployed in advance for each vehicle’s request when the cache needs to be updated on system performance. In addition, we evaluate the effectiveness of the proposed scheme. Our proposed scheme can achieve the best cache hit ratio and decrease caching costs compared to the existing mobility-aware in-order caching schemes.
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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 | 0.001 |
| 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) | 0.000 | 0.000 |
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".