Smart Proactive Caching: Empower the Video Delivery for Autonomous Vehicles in ICN-Based Networks
Bibliographic record
Abstract
The recent advances in vehicular communications and networking are bringing self-driving vehicles closer to reality. Once full automation (i.e., levels 4 and 5 as defined in the five levels of autonomous driving) becomes a reality, entertainment services for both drivers and passengers will shift from listening to radios (or music) to watching videos. Hence, how to improve the quality of experience (QoE) for autonomous vehicle (AV) users, and to reduce the network load will become a crucial problem. Information-centric networking (ICN) is seen as one of the potential paradigms for next-generation networks, and could potentially be used for content distribution in vehicular networks. Caching, an important feature in ICN-based networks, is an efficient way to reduce network load, and to improve QoE for users. However, traditional reactive caching approaches are inefficient for AV users due to the high delay caused by their high speed. In this paper, we propose a novel hierarchical proactive caching approach that considers both users' future demands and AV user mobility. The proposed approach uses the non-negative matrix factorization (NMF) technique to predict user's preferences which are then used to predict users' future demands by considering the historical popularity of videos. A user mobility prediction model is used to predict the AV users' next location based on the current location and the planned route information which can be retrieved from the self-driving system. Based on the predicted users' future demands and locations of AVs, the proposed caching approach can proactively cache videos that are likely to get requested at the next base station (BS) or roadside unit (RSU) that the users are moving to. The proposed approach has been evaluated under two scenarios: a highway scenario and a grid street scenario. Results show that the proposed approach can significantly improve the efficiency of caching in terms of hit ratio and the average number of hops.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".