Adaptive Data Transmission and Computing for Vehicles in the Internet-of-Intelligence
Bibliographic record
Abstract
Efficient scheduling of vehicle resources is of great significance to guarantee vehicle safety and to achieve a higher level of automated driving. Considering the performance fluctuations in data transmission and processing during driving, this paper proposes an adaptive data transmission and computation optimization scheme, where the concept of the Internet-of-Intelligence is introduced to improve the resource decision-making efficiency through knowledge sharing instead of data sharing among vehicles. Specifically, the joint optimization problem is formulated to minimize the long-term energy consumption with the consideration of the average queuing latency guarantees. To provide a stable and fast solution, Lyapunov optimization method is first leveraged to transform the formulated stochastic problem into a series of short-term deterministic optimization subproblems. Afterwards, both the optimization-based solution and the learning-based solution are presented to fully illustrate the performance advantages of Internet-of-Intelligence applied to vehicle networks. The former can output the global optimal solution by iteration, while the latter aims at accelerating the distributed optimization decision-making through building a fast deep reinforcement learning framework based on shared knowledge among vehicles. Simulation results show the advantages of the proposed scheme in stability, energy consumption, and latency, and it also verifies the convergence speed and training accuracy of the proposed fast deep reinforcement learning framework.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".