Software-Defined Vehicular Networks with Caching and Computing for Delay-Tolerant Data Traffic
Why this work is in the frame
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Bibliographic record
Abstract
With the explosion in the number of connected devices and Internet of Things (IoT) services in smart city, the challenges to meet the demands from both data traffic delivery and information processing are increasingly prominent. Meanwhile, the connected vehicle networks have become an essential part in smart city, bringing massive data traffic as well as significant networking, caching and computing resources. In this paper, we propose a novel vehicle network architecture, mitigating the network congestion with the joint optimization of networking, caching and computing. Cloud computing at the data centers as well as mobile edge computing (MEC) at the evolved node Bs (eNodeBs) and on-board units (OBUs) are taken as the paradigms to provide caching and computing resources. The programmable control principle originated from software-defined networking (SDN) paradigm has been introduced to facilitate the system architecture and resource integration. With the careful modeling of the services, the vehicle mobility and the system state, a joint resource management scheme is proposed and formulated as a partially observable Markov decision process (POMDP) to minimize system cost, which consists of both network overhead and execution time of computing tasks. Extensive simulation results with different system parameters reveal that the proposed scheme could significantly improve the system performance compared to the existing schemes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it