Optimal Distributed Vertical Handoff Strategies in Vehicular Heterogeneous Networks
Bibliographic record
Abstract
This paper addresses the problem of optimal vertical handoff (VHO) in a vehicular network setting. The VHO objective can be minimizing the data transfer time or alternatively minimizing the cost of transmitting traffic. As a framework for performance evaluations, we first analyze a heterogeneous network consisting of a wide-area cellular network interworking with wireless local area networks (WLAN) with fixed inter-distance between access points (APs) placed along roadsides. We further analyze a scenario with random inter-distance between WLAN APs. In both aforementioned cases, only Vehicle-to-Infrastructure (V2I) capability is assumed. We show that in order to minimize the cost of transmission or alternatively transmission time, performing VHOs is an appropriate choice at lower speeds, whereas it would be better to avoid VHO and stay in the cellular network at higher speeds. We further generalize our study, to investigate the VHO strategies in a random inter-distance scenario with both V2I and Vehicle-to-Vehicle (V2V) communication capabilities. We demonstrate that the combination of WLAN plus cellular plus ad hoc networking outperforms any other networking strategies considered in this work in terms of transmission times and transmission costs. The presented results provide insightful guidelines for optimal VHO decision making based on the characteristics of the network as well as the user mobility profile.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".