Analyzing IEEE 802.11g and IEEE 802.16e Technologies for Single-Hop Inter-Vehicle Communication
Bibliographic record
Abstract
This chapter analyzes two prominent technologies, IEEE 802.11g (WiFi) and IEEE 802.16e (WiMAX), for single-hop inter-vehicular communication (SIVC). We begin our analysis by comparing the physical and MAC layers of both standards. Following this, we simulate two scenarios, one with IEEE 802.11g and the other with IEEE 802.16e, in a single-hop inter-vehicular communication network. In both scenarios, the Location-Based Routing Algorithm with Cluster-Based Flooding (LORA-CBF) was employed to create a hierarchical vehicular organization that acts as a cluster-head with its corresponding member nodes. The simulation scenarios consist of five different node sizes of 20, 40, 60, 80 and 100 vehicles, respectively. We propose a novel simulation model that is suitable for mesh topologies in WiMAX networks and provide preliminary results in terms of delay, load and throughput for single-hop inter-vehicle communication.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".