An Optimized Link Duration-Based Mobility Management Scheme for Connected Vehicular Networks
Bibliographic record
Abstract
Recently, both academia and automotive industries have shown an increasing interest in autonomous driving and real-time services for new modern vehicles, including adaptive cruise control, traffic conditions, emergency alerts, and infotainment services. Wireless mobile communication is vital for the efficient delivery of such services to drivers on the road. However, the vehicles' high speed and rapid changes in their movements affect the performance of traditional wireless communication. Therefore, an efficient mobility management protocol is needed for vehicular networks. Conventional mobility management schemes yield high communication overhead and packet drop during the handover process, as it doesn't consider vehicles movements characteristic and projections. In this paper, we propose an optimized link duration-based mobility management scheme for vehicular networks, in order to reduce the network communication overhead while maintaining high packet delivery ratio and low End-to-End delay. Our model estimates the link stability between a vehicle and an access router to dynamically adjust the registration time between them. The proposed scheme is evaluated in terms of overhead, End-to-End delay, and packet delivery ratio. Simulation results indicate that the proposed scheme succeeds in reducing the communication overhead by 45% while increasing the packet delivery ratio in Urban mobility environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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".