Efficient data gathering and position dissemination protocols for heterogeneous vehicle ad hoc and sensor networks
Bibliographic record
Abstract
Data gathering and localization algorithms for vehicular ad hoc networks (VANets) are envisioned as key technologies for the near future. These technologies will pave the way for a number of potential applications that required precise positioning information that GPS systems are not able to provide. However, high mobility of vehicles in a VANet introduces frequent topology changes that negatively affect existing solutions and poses significant challenges to developing effective localization and data gathering mechanisms. In this paper, we propose a new data gathering and localization dissemination protocol that uses an existing time-space synchronization technique to efficiently collect data from heterogeneous networks composed of wireless sensor nodes and vehicles in a VANet. Localization information is disseminated from vehicles to sensor nodes, which can compute their positions without the need of energy-hungry GPS modules. The focus of this paper is to provide a mechanism that is able to simultaneously collect data from the nodes, relay data to interested nodes, and to disseminate localization information that will aid the nodes to estimate their positions.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".