Name
Abstract | ||
|---|---|---|
One of the challenges related to the investigation of vehicular networks is associated with predicting a network state regarding both short-term and long-term network evolutionary changes. This paper analyzes a case in which vehicles are located on a straight road, and the connectivity state between two consecutive cars is determined by the Markov chain model with two states. The transition probabilities of the considered model are explicitly expressed in terms of known parameters of the network using the Wang–Moayery model. Within the presented model, the network evolution is described in terms of determinative parameters, such as average link duration, average cluster lifetime, and a clusters’ existence probability between two fixed moments of time. In support of the theoretically obtained probabilistic distributions, the results of numerical simulations are provided. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TVT.2019.2927389 | IEEE Transactions on Vehicular Technology |
Keywords | Field | DocType |
Automobiles,Mathematical model,Roads,Markov processes,Probability,Probabilistic logic,Machine learning | Cluster (physics),Markov process,Computer science,Markov chain,Algorithm,Vehicle networks,Computer network,Probabilistic logic,Link duration,Vehicular ad hoc network | Journal |
Volume | Issue | ISSN |
68 | 9 | 0018-9545 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gleb Dubosarskii | 1 | 0 | 1.01 |
| Serguei Primak | 2 | 104 | 18.90 |
| Xianbin Wang | 3 | 2365 | 223.86 |