An Efficient Movement-Based Handover Prediction Scheme for Hierarchical Mobile IPv6 in VANETs
Why this work is in the frame
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Bibliographic record
Abstract
Wireless mobile communication in vehicular networks is essential for the content delivery of road safety and infotainment services to drivers. However, the vehicles' high mobility and topology changes affect the performance of traditional mobility management protocols over VANETs. Therefore, an efficient mobility management solution that mitigates the challenges of vehicles' mobility is needed. In this paper, we present a predictive hierarchical handover protocol for mobile IP in vehicular networks. We combine the stochastic probability analysis of a hidden Markov model, and the vehicles' movement projection to predict the next handoff. We evaluate the performance of our protocol against different mobile handover protocols using the network simulator NS-2, and various mobility traces. Furthermore, we assess the impact of the different type of observations on the prediction model. Our results showed that our predictive module outperforms all other handover protocols in reducing the handover latency and packet loss.
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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 | 0.000 |
| 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 it