Application-based packet routing in vehicular networks
Bibliographic record
Abstract
A wide range of vehicular applications require packet routing mechanisms and protocols for efficient, reliable and robust delivery of data packets over vehicles, from a single source or from multiple sources to either a specific destination or multiple destinations in a specific region. All vehicular applications, e.g., non-safety applications and safety applications, have their own specific challenges, considerations and Quality of Service (QoS) requirements which are different from those of other vehicular applications. Hence, we believe that the design of packet routing mechanisms and protocols for each vehicular application should be application-specific. In non-safety applications, to access the backhaul network through the infrastructure on the roadside, so-called Road-side Units (RSUs), since many parts of the network may not be directly covered by RSUs, appropriate routing protocols need to be designed and employed. In this thesis, we developed a single-technology routing protocol for Vehicular Ad hoc Networks (VANETs), Connectivity-aware Minimum-delay Geographic Routing (CMGR), which adapts well to continuously changing network status in such networks. As the next step we studied how packet routing mechanisms and protocols should be adapted to heterogeneous environments. In this regard, we developed optimal Vertical Hand-Off (VHO) strategies for vehicular heterogeneous networks when RSUs directly cover all parts of the vehicular network under study. Next, we turned our attention to the case where some parts of the network are not directly covered by any RSU and proposed a Hybrid Multi-Technology Routing (HMTR) protocol to consider different combinations of wireless technologies in intermediate hops when establishing routes from vehicular end-users to RSUs. In safety applications the notification of hazardous situations needs to be sent from the hazard-detecting vehicle to every other vehicle in the neighborhood, so-called data dissemination. Fully ad hoc data dissemination mechanisms have gained more acceptance due to their robustness and avoiding the excessive costs of infrastructure deployment and maintenance. In this regard, one of the main challenges is to overcome the packet delivery failures at intersections in the ad hoc manner. In this regard, we developed a fully ad hoc data dissemination mechanism, Enhanced Intersection-mode Data Dissemination (EIDD), which provides reliable packet delivery at intersections.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".