A performance modeling of connectivity in vehicular ad hoc networks (VANETs)
Bibliographic record
Abstract
An emerging new type of ad hoc networks is Vehicular Ad hoc NETworks (VANETs) which envision Inter-Vehicle Communications. Since, nodes in VANETs are both mobile as well as carrier of information; the network may not have full communication connectivity all the time and they may form several clusters where the nodes in each cluster may communicate with each other directly or indirectly. Multi-clustering happens whenever the minimum distance between two adjacent nodes becomes more than the transmission range of a node. Therefore, two important performance measures which affect the functionality in VANETs are communications connectivity and path availability . In this thesis, we study the statistical properties of these performance measures in VANETs at the steady state. First, it is assumed that the nodes travel along a multi-lane highway which allows vehicles to overtake each other. We derive the probability distributions of the node population size and node's location in the highway segments. Then, we determine the mean population size in a cluster and probability that nodes will form a single cluster. Then we extend the single highway model to a network of highways with arbitrary topology. We determine the joint distribution of the node populations in the highways' segments by application of the BCMP theorem. We model the number of clusters within the node population in a network path as a Markovian birth-death process. This model enables derivation of the probability distribution of the number of clusters and determination of mean durations of continuous communication path availability and unavailability times as functions of mobility and node arrival parameters. At the end, mean packet delay is presented for end to end communication in a path. We give numerical results which illustrate the effect of mobility on continuous communication path availability and communication delay. The results of this work may be helpful in studying the optimal node transmission range assignment, routing algorithms, network throughput, optimization of cross layer design schemes and MAC protocols in VANETs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 teacher head, 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".