Near real-time big data analysis on vehicular networks

Abstract

In this cutthroat era of 21st Century Traffic information is considered as one of the prominent valuable resources in vehicular networks for big data analysis. In order to effectively utilize the acquired resources, big data analysis in near real time will be an appropriate way to produce valuable information from raw data. In order to exhibit the importance of big data investigation, an efficient architecture has been proposed for near real time big data analysis in vehicular networks, which indeed will keep pace with the latest trends and development with respect to emerging big-data paradigm. The proposed architecture, comprises centralized data storage mechanism for batch processing and distributed data storage mechanism for streaming processing in real time analysis. Furthermore a work flow model has also been designed for big data architecture to examine streaming data in near real time process. Furthermore, an algorithm is designed for organizing the vehicle flow in a particular location or place. The proposed system model is for optimal utilization of the massive data set, meant for streaming data in near real time process intended for ITS (Intelligent Transportation System) in a vehicular environment.