A fast location-based handoff scheme for vehicular networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
IEEE 802.11 is an economical and efficient standard that has been applied to vehicular networks. The long handoff latency of the traditional handoff scheme for IEEE 802.11, however, becomes an important issue for seamless connections in vehicular environments, as more handoffs may be triggered due to higher mobility of vehicles. This paper presents a new fast location-based handoff scheme particularly designed for vehicular networks. With the position and direction of a vehicle and locations of its surrounding access points (APs), our algorithm can accurately predict the next AP that the vehicle may visit. Time spent on scanning APs in handoff procedures is therefore significantly saved. The AP selection scheme can also reduce the total number of handoffs by selecting APs on the vehicle's advancing path. Finally, simulation results demonstrate that the proposed scheme attains low prediction error rates, low layer-2 handoff latency and the reduced number of unnecessary handoffs.
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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