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Real Time Vehicular Data Analytics Utilising Bigdata Platforms and Cost Effective ECU Networks
Background/Objectives: This paper is aimed at performing real time bigdata analytics on vehicular data collected from a network of ECUs (Electronic Control Unit) in cooperated into the different automobiles. Methods/Statistical Analysis: The analytics has been performed by building a software model that is capable of handling the vehicular data in real time. Bigdata platforms like hadoop, Apache Storm, Apache Spark(real time streaming), Kafka are utilised here. Automotive sensor data from different Electronic Control Units are collected into a central data server and this data is pushed to kafka, from which the real time streaming models pulls the data and perform analysis. Findings: Automotive industry has undergone a drastic revolutionised innovation in the past decade in all of its respective segments. The industry had started utilizing the computational and mathematical aspects from top to bottom in its design strategies to achieve greater reliability on its products out on roads. Latest advancements in this field is the fully autonomous car. Today an automotive is a collection of innumerable sensors and microcontrollers which are under the command of the master ECU. A network of ECUs connected across the globe is a source tap of bigdata. Leveraging the new sources of bigdata by automotive giants boost vehicle performance, enhance loco driver experience, accelerated product designs. Statistical Projections reveal that automotive industry is likely to be the 2nd largest generator of data by mid of 2016. The contribution of this paper to the automotive industry is the real time vehicle monitoring utilizing Bigdata platforms. This can contribute to better customer-industry relations. Applications/Improvements:The model developed in this paper can contribute a lot to the automobile industry as it facilitates real time monitoring of the vehicles. This can improve customer-industry relation.
ECU, Hadoop, Kafka, Spark, Storm.
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