Applications of Data Mining Techniques for Vehicular Ad hoc Networks

TL;DR

This paper presents a taxonomy and classification of data mining techniques applied to vehicular ad hoc networks, highlighting research methodologies and enabling comparison of different approaches.

Contribution

It introduces a comprehensive taxonomy of data mining methods used in VANETs, including various techniques and their characteristics, to facilitate research comparison.

Findings

Taxonomy covers preprocessing, outlier detection, clustering, classification.

Includes centralized, distributed, offline, and online techniques.

Helps researchers compare methodologies in VANET data mining.

Abstract

Due to the recent advances in vehicular ad hoc networks (VANETs), smart applications have been incorporating the data generated from these networks to provide quality of life services. In this paper, we have proposed taxonomy of data mining techniques that have been applied in this domain in addition to a classification of these techniques. Our contribution is to highlight the research methodologies in the literature and allow for comparing among them using different characteristics. The proposed taxonomy covers elementary data mining techniques such as: preprocessing, outlier detection, clustering, and classification of data. In addition, it covers centralized, distributed, offline, and online techniques from the literature.