Modeling Enhancements in Routing Protocols under Mobility and Scalability Constraints in VANETs
Bibliographic record
Abstract
This paper presents mathematical framework for calculating transmission probability in IEEE 802.11 p based networks at Medium access control (MAC) layer, mathematical framework for calculating energy costs of the chosen routing protocols at network layer, and enhancements in optimized link state routing (OLSR), dynamic source routing, (DSR) and fish-eye state routing (FSR) to tackle delay in vehicular ad hoc networks (VANETs). Besides the enhancements, we analyze ad hoc ondemand distance vector (AODV) along with OLSR, DSR, and FSR as well. To evaluate the effect of our proposed transmission probabilities in the selected routing protocols, we choose network throughput, end-to-end delay (E2ED), and normalized routing load (NRL) as performance metrics. We also investigate the effect of different mobilities as well as scalabilities on the overall efficiency of the enhanced and default versions of the selected protocols. Simulations results which are conducted in NS-2 show that overall DSR-mod outperforms rest of the protocols.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".