An Energy-Efficient Proactive Handover Scheme for Vehicular Networks Based on Passive RSU Detection
Why this work is in the frame
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Bibliographic record
Abstract
Recently, the Vehicular Network (VN) has received a lot of attention from researchers around the world. By allowing wireless communication, VNs enable information exchange among vehicles, which in turn has allowed drivers to become more aware of their surrounding road conditions. Accordingly, road safety is improved. However, due to the fast speed and frequent changes of direction of vehicles, the network topology of VNs is transient in nature. Hence, achieving efficient data dissemination/content delivery is a critical issue in the VNs-environment. In this article, we will introduce a novel passive roadside unit (RSU) detection-based proactive (PRDP) handover scenario. Consequently, the overhead of the handover process can be reduced, and the probability of successfully established connections can be improved. More precisely, by taking advantage of the Doppler effects of the received beacon signal, the passive RSU detection (PRD) scheme is derived by the maximum likelihood estimation function. Then, in combination with the extended Kalman filter (EKF), the PRDP handover protocol is designed to improve the energy efficiency of the handover procedure in the VNs-environment. We conduct intensive simulations to verify the proposed RSU detection scheme, and the experimental results further evaluate the performance of the proposed energy-efficient proactive handover protocol.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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