Reverse Offloading for Latency Minimization in Vehicular Edge Computing
Bibliographic record
Abstract
The safety of autonomous driving can be improved with the support of Cooperative Vehicle-Infrastructure System (CVIS) and Vehicular Edge Computing (VEC), which benefit greatly from crowdsensing of CVIS and accurate decision in a short deadline of VEC. In the CVIS, the vehicles will upload the crowdsensing data to the VEC server for data fusion and tasks generating. However, with the ever-increasing number of vehicles, the VEC server cannot undertake massive computation-intensive tasks due to the limited edge computing capabilities. In this paper, we propose a reverse offloading framework to fully utilize the computation resource of vehicles to relieve the burden of the VEC server in a multi-vehicle mobile edge network. First, the system latency minimization problem is formulated as a mixed integer nonlinear programming problem by optimizing reverse offloading decisions and the communication and computation resources allocation. Next, the original problem is transformed into an equivalent weighted-sum optimization problem, which can be decoupled as two subproblems, i.e., resource allocation and decision selection subproblems. The closed-form expressions for the optimal resource allocation are derived by the dual decomposition method in a distributed fashion. Moreover, a low complexity greedy based efficient searching (GES) algorithm is proposed to obtain the reverse offloading decision strategies. Simulation results show that the proposed algorithm can significantly improve the performance compared with other baseline schemes.
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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 | 0.000 | 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".