A Vehicular Task Offloading Method With Eliminating Redundant Tasks in 5G HetNets
Bibliographic record
Abstract
The combination of mobile edge computing and 5G heterogeneous networks (5G HetNets) provides new vehicular task offloading research solutions. Most existing task offloading studies assume that vehicle tasks are unique and there are no redundant tasks between vehicles. However, there is a duplication of tasks for vehicles within the same base station. That causes a waste of computing resources and increases task offloading costs. To address this problem, this paper proposes the task offloading algorithm TOERT to eliminate redundant tasks in 5G HetNets. The TOERT algorithm is designed to eliminate redundant tasks, improve vehicle task completion rates and reduce offloading costs. Specifically, we consider two cases of redundant tasks within the macro cell base station (MCBS). When the task results have been stored in the MCBS, vehicles directly agree on the transaction price with the MCBS to obtain the task results. The MCBS first eliminates redundant tasks between vehicles when task results are not stored. Then, the MCBS determines the appropriate small cell base station (SCBS) to participate in the partial offloading. Finally, the vehicles negotiate with the MCBS to obtain task results. Against the other five algorithms considered for comparison purposes, the TOERT algorithm effectively eliminates redundant tasks, improves the task completion rate and increases the benefits of both the vehicles and the MCBS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".