Towards provisioning vehicle-based rural information services
Bibliographic record
Abstract
Rural areas often lack extensive technological support and usually do not have a high priority in governmental investments due to economic reasons. As the scale of information service provisioning - with data sensing and delivery as primary elements - grows national, there is a matching need to extend these services to rural areas. Smart Vehicular networks present cost-effective, mobile coverage for rural areas. The sensing and buffering resources of vehicles can be utilized in collecting and relaying data. Being delay-tolerant in nature, the data need for some information services, such as environmental monitoring applications, can be sensed by on-vehicle sensors and delivered by vehicles to the collecting destination/sink. In traditional store-carry-and-forward data delivery techniques, a vehicle can continue to carry data even if it is heading away from the destination direction. Such cases may lead to excessive delays and eventual packet dropping. As a part of the vehicular network, road side units (RSUs) are deployed at intersections with no/limited backbone communication at rural areas. By utilizing these RSUs, we propose an infrastructure-assisted data delivery (IADD) scheme that utilizes vehicles headings to enhance delay-tolerant rural information services and improve data delivery ratio and packet delay. Our scheme is evaluated via extensive simulations, carried on NS-2. Our experiments show that IADD achieves significant performance improvements in terms of delivery ratio and delay bounds compared to the traditional store-carry-and-forward technique.
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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 | 0.003 |
| 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".