Applications of Data Mining Techniques for Vehicular Ad hoc Networks
This provides a framework for researchers in VANETs to compare methodologies, but it is incremental as it synthesizes existing literature without introducing new techniques.
The paper tackles the problem of organizing and comparing data mining techniques for vehicular ad hoc networks (VANETs) by proposing a taxonomy and classification, resulting in a structured overview of methods like preprocessing, outlier detection, clustering, and classification.
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.