Security and Connectivity Analysis in Vehicular Communication Networks
Bibliographic record
Abstract
Reliable Vehicular Ad-Hoc Networks (VANETs) require secured uninterrupted uplink and downlink connectivity to guarantee secure ubiquitous vehicular communications. VANET mobility, multi-fading wireless, and radio channels could result in unsecured and disrupted vehicular communications, isolating some vehicle nodes and making them vulnerable to security attacks. A VANET is considered to be connected and secured if there is a secured path connecting any pair of Communication-Enabled Vehicles (CEVs) in this network. Among many parameters, VANET connectivity depends on two main elements: communication transmission range and statistical distribution characterizing inter-vehicle spacing. To guarantee persistent VANET connectivity, a vehicle transmission radio range must be set properly based on the characteristic of the statistical distribution modeling the inter-vehicle spacing. This chapter analyzes three inter-vehicle spacing models based on exponential, Generalized Extreme Value (GEV), and Exponential with Robustness Factor (EwRF) statistical distributions. Based on vehicle nodes spatial density on a road segment, each vehicle node can adjust its transmission range to increase network connectivity and guarantee ubiquitous vehicular communications. Communications among vehicle nodes are secured through trusted Road-Side Units (RSUs) which distribute efficiently secret keys to vehicle nodes under their coverage to establish secure communication sessions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".