Журнал компьютерных наук и системной биологии

Analysing Big Data in VANET via HADOOP Framework

Abstract

Rahul Kumar Chawda and Ghanshyam Thakur

Objectives: Illustration of features of big data which make the Vehicular Ad Hoc Network communication more accurate and precise. The open framework like Hadoop and Map Reduce which are used in big data for managing, storing and accessing of information is also provided.

Methods/Statistical analysis: The technology of big data is continuously growing and with its rapid increase it is gaining the attention of the researchers. The data is analyzed and outputted in a form that it helps in making quick responses and action in real time environment like Vehicular Ad hoc network. Big data helps in gaining the insight view of the stored, operational and altered data, to improve the traffic conditions. When the Vehicular Ad Hoc Network and the big data are combined, it helps in maintaining the large amount of traffic triggers very easily as the data mining process in big data helps to make quick decisions on the basis of statistics or graph, which are the result of analysis of data.

Findings: Big data and Hadoop cannot be compared because these two are reciprocal to each other. Big data can be considered as a problem and Hadoop can be a solution to it. The combination of Hadoop and Big data in Vehicular Ad Hoc Network provides services useful for number of applications.

Application/Improvements: A lot of applications can be made in future which helps in making the big data analysis much easier and helps in making the on-road condition more secure.