iCARII: Intersection-based connectivity aware routing in vehicular networks
Bibliographic record
Abstract
Vehicular Ad hoc Network (VANET) has been gaining the attention from the academic and industry communities due to the promising applications in road safety, traffic management and passenger comfort. However, the short vehicle-to-vehicle (V2V) communication range and the nature of vehicle mobility impose many challenges in packet routing and, accordingly, infrastructure-based applications, e.g., Internet access. For more reliable communication, automobile manufacturers have started the investigation to deploy cellular networks, such as LTE, to support in-car Internet access. In addition to the high communication cost to the end-user, using centric cellular networks for vehicular communication applications causes data explosion and network overload. A feasible solution is using VANETs for mobile data offloading when connectivity to infrastructure is guaranteed, which requires an efficient routing protocol with global connectivity awareness. In this paper, we propose a novel infrastructure-based, dynamic, and connectivity-aware routing protocol, iCARII, to enable infotainment applications and Internet access in an urban environment. iCARII aims to improve VANETs routing performance by enabling selecting roads with guaranteed connectivity and reduced delivery delay. Detailed analysis and simulation-based evaluations of iCARII demonstrate the significant improvement of VANET performance in terms of packet delivery ratio and end-to-end delay with a negligible cost of routing overhead.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 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".