The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Optimizing Vehicular Network Architecture and Communication Models With Machine Learning Approach
Abstract
Vehicular networks are a digital web that links the entities of vehicles, infrastructure, and information to enable road transportation safer, more efficient, and amicable to the users. It is a more comprehensive system comprising different communication models and architecture, where the latter supplies a supportive framework for the communication models. This chapter proposes a machine learning approach to determine a more suitable combination between the VNA & CM to optimize the efficacy of the VL. The ML algorithm of DL networks with multi-layers is leveraged to analyze factors such as traffic patterns, network topology, communication range, and application needs to identify the most appropriate combination of VNA and CM. The capability of the ML approach is validated using the performance metrics in comparison with other learning algorithms to identify the limitations of the proposed model. This machine learning-based decision model shall also be employed as a predictive model to determine the optimal combination of VNA & CM for optimal planning and implementation of VN.
Related Content
|
Elena Fernández Gascueña, Enriqueta Villanueva-Montero, María García de Blanes Sebastián.
© 2026.
32 pages.
|
|
Francisco José Martínez Carmona, Rubén Madrigal Cerezo.
© 2026.
20 pages.
|
|
Alexandra Martin Rodriguez, Rubén Madrigal Cerezo.
© 2026.
26 pages.
|
|
María Patricia Soroa de Carlos, Javier Saiz Briones.
© 2026.
28 pages.
|
|
José Ramón Sarmiento-Guede, Alberto Azuara-Grande.
© 2026.
24 pages.
|
|
David de Matías Batalla, Rubén Nicolás Sans.
© 2026.
24 pages.
|
|
Felipe Ignacio Garcia-Soriano.
© 2026.
34 pages.
|
|
|