Using passive RFID tags for vehicle-assisted data dissemination in intelligent transportation systems
Bibliographic record
Abstract
Intelligent transportation systems (ITS) in the form of vehicular adhoc networks (VANETs) have engaged significant interest from the academic, industry and government sectors. Data dissemination is of the utmost importance for the day-to-day operation of ITS applications. Numerous standards, architectures and communication protocols have been anticipated for data diffusion in ITS applications. However, existing schemes are based on an essential condition that the relaying vehicle has to be equipped with an active communication module - termed Intelligent Vehicle (IV). One of the major drawbacks of these schemes is that they do not exploit the potentially large number of non-intelligent vehicles (non-IVs), i.e., the vehicles without any active communication module for relaying and diffusion purposes. In this paper, we fill this gap by proposing a novel data dissemination scheme utilizing a non-IV to act as a data ferry. The non-IV is tagged with a low-cost passive RFID tag whereas the IV is equipped with an embedded RFID reader. The non-IVs ubiquitously store and carry the events in the passive tags, as recorded by the IV or by the roadside equipment, as they maneuver around the city blocks. Two system configurations, namely co-operative and stand-alone with and without vehicle-to-infrastructure (V2I) support, respectively, are also proposed. Simulation results show the proposed scheme's effectiveness and performance superiority over the existing active-based data dissemination methods.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".