Fault-Tolerance Evaluation of VANET Under Different Data Dissemination Models
Bibliographic record
Abstract
Mobile Ad hoc Networks (MANETs) is a collection of wireless mobile nodes, which form temporary networks over infrastructure-less environments. Over the last few years, Vehicular Ad hoc Network (VANET) technology has been considered as an active research area. VANET is a sub class of MANET with some unique characteristics such as fast vehicle speed, frequent topology changes and restricted mobility on roads. The communication between vehicles provides a new avenue for different types of applications such as safety, traffic management, entertainment, etc. These applications are categorized based on how data is transmitted from source to destination. The performance of such applications does not only depend on routing protocols, but also on the data dissemination mechanism. Data dissemination in VANETs is a significant factor, which can be accomplished using four different models: Dynamic Source Dynamic Destination (DSDD), Dynamic Source Static Destination (DSSD), Static Source Dynamic Destination (SSDD), and Static Source Static Destination (SSSD). Each one of these models is suitable for specific types of applications. In this paper, the authors study and evaluate the fault-tolerance of VANET under different data dissemination techniques in terms of throughput, average End-to-End delay, and percentage of packet loss. the authors used NCTUns 6.0 network simulator and IEEE 802.11p wireless communication standard. Their findings show that DSDV is more fault-tolerant than both DSR and AODV in terms of packet loss percentage for all dissemination techniques. However, AODV shows better performance in average End-to-End delay and throughput under DSDD and SSDD techniques.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".