WLAN-WiMAX Double-Technology Routing for Vehicular Networks
Bibliographic record
Abstract
In this paper, we propose a novel routing protocol, WLAN-WiMAX Double-Technology Routing (WWDTR), for routing packets in the heterogeneous vehicular network. Any given route in the protocol can be generally formed of a combination of WLAN and WiMAX hops. WWDTR uses the position-based routing approach over the parts of the route in which packets are forwarded via WLAN radios, in order to handle relatively fast changes of the topology with respect to the shorter transmission ranges of WLAN-enabled vehicles. On the other hand, topology-based routing is employed over the parts of the route in which packets are forwarded via WiMAX radios, given the more stable routes comprised of WiMAX-enabled vehicles, thus overall yielding a hybrid routing scheme. The route selection logic takes both operators' and subscribers' preferences such as QoS and network utilization into account. Furthermore, we propose network architecture to facilitate forwarding packets towards the access network of a given operator over routes that partly utilize relaying over other operators' links. To the best of our knowledge, WWDTR is the first multi-technology multi-operator routing solution for heterogeneous vehicular networks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".