The Intelligent Mechanism for Data Collection and Data Mining in the Vehicular Ad-Hoc Networks (VANETs) Based on Big-Data-Driven

Abstract

Big data technology has attracted the main attention of researchers in almost all sciences. The Vehicular Ad-Hoc Network (VANET) enables information exchange between vehicles, other devices, and public networks, playing a key role in road safety and intelligent transportation systems. With the proliferation of connected vehicles and the development of novel mobile apps and technologies, VANETs will generate vast quantities of data that need to be transmitted quickly and reliably. Furthermore, analyzing a wide range of data types can enhance VANET’s performance. By utilizing big data technologies, the Ad-Hoc Vehicular Network can extract valuable insights from a large amount of operational data, thus improving traffic management processes, including planning, engineering, and operations. VANETs have access to big data during real-time operations. This paper presents VANET features as big data features in the literature, followed by a discussion of methods for utilizing big data to study VANET features. Combining automotive ad networks and big data facilitates the easy management of a large number of driving factors, as the data mining process in big data enables quick decision-making based on statistical analysis or graphical representations of data.