An Improved DBSCAN and Multi-Agent Based Task Offloading Mechanism for 6G-Enabled Internet of Vehicles
Bibliographic record
Abstract
High mobility of Internet of Vehicles (IoV) brings rapidly changing network topology, and massive data produced by vehicles aggravate heavy burden to the network. These may lead unreliable and high latency of data transmission and processing, which is not facilitate the application and popularization of automatic driving. Evolutions of intelligent vehicles and edge intelligence promising technologies enable vehicles as agents. Vehicles have abilities to act as aided Mobile Edge Computing (MEC) servers to support ultra-low communication and computing latency and super-high reliability data transmission and processing. In this paper, we elect some vehicles as aided MEC servers and design an improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm to involve more scattered vehicles for clustering. We adopt a Multi- Multi matching algorithm to pair vehicles and the aided MEC server, and designed a multi -agent- based task offloading mechanism to reduce latency and improve resource utilization efficiency. Furthermore, a reward mechanism is proposed to stimulate vehicles to be aided MEC servers instead of refusing to provide services. Evaluation results verify the proposed offloading method could effectively reduce the average delay, improve computing resources utilization and increase the benefit of aided MEC servers and service providers.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".