Joint Computation Offloading and Data Caching in Multi-Access Edge Computing Enabled Internet of Vehicles
Bibliographic record
Abstract
Internet of Vehicles (IoV) has attracted global research interests across extensive applications. Due to the significant increase in the number of vehicles accessing the Internet, there are several challenges in designing efficient task offloading and data caching strategies to improve the utilization of the network resource and provide the users with high-quality services. To this end, this study proposes the task offloading and resource allocation schemes, including the selection of execution mode, data transmission path, the assignment of the sub-channels, the strategies of caching and caching updating in a Multi-Access Edge Computing (MEC) enabled IoV system with multiple mobile vehicles equipped with the capacity of energy harvesting. Specifically, the downlink relevant data or the uplink offloaded data can be transmitted through either the Macro Base Station(MBS) or the Road Side Unit(RSU). Also, we consider two different situations: off-peak hours and peak hours, in which the execution mode is different. In off-peak hours, the tasks can directly offload to the MEC server, and the average execution delay minimization problem is modelled as an integer programming problem, which is solved by Simulated Annealing Genetic Algorithm (SAGA). In peak hours, the tasks can be either executed locally or offloaded to the MEC server, and the formulated problem are more complicated, which are solved by Deep Q Network (DQN). Finally, a series of simulations are conducted to demonstrate the efficiency of the proposed schemes.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".