Real-Time Data Transformation in Connected Vehicles: A Systematic Analysis of Architectures, Methods, and Applications

Anush kumar Thati

doi:10.32628/CSEIT241061188

Authors

Anush kumar Thati Ford Motor Company, USA Author

DOI:

https://doi.org/10.32628/CSEIT241061188

Keywords:

Connected Vehicles, Real-Time Data Processing, Edge-Cloud Computing, Vehicle Telematics, Intelligent Transportation Systems

Abstract

The emerging landscape of connected vehicles has introduced unprecedented challenges in processing and utilizing vast streams of real-time data. This article presents a comprehensive framework for real-time data transformation in connected vehicle environments, addressing the critical aspects of data processing architectures, analytical methodologies, and practical implementations. The article examines the integration of edge computing, cloud-based solutions, and hybrid architectures to optimize data transformation workflows while minimizing latency and bandwidth constraints. The article analyzes various data types generated by connected vehicles, including telemetry, diagnostics, and user-generated content, and explores their transformation requirements for enabling advanced functionalities such as predictive maintenance, traffic optimization, and enhanced driver assistance systems. Through multiple industry case studies, the article demonstrates the practical application of proposed frameworks and their impact on operational efficiency, safety metrics, and overall vehicle performance. Our findings highlight the significance of balanced architectural choices, the role of machine learning in data transformation processes, and the importance of addressing security and scalability challenges. This article contributes to the growing body of knowledge in connected vehicle technologies while providing practical insights for automotive industry practitioners implementing real-time data transformation solutions.

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References

Xu, W., Zhou, H., Cheng, N., Lyu, F., Shi, W., Chen, J., & Shen, X. (2018). "Internet of Vehicles in Big Data Era," IEEE/CAA Journal of Automatica Sinica, vol. 5, no. 1, pp. 19-35. https://ieee-jas.net/en/article/doi/10.1109/JAS.2017.7510736 DOI: https://doi.org/10.1109/JAS.2017.7510736

Warren, J., & Marz, N. (2015). “Big Data: Principles and Best Practices of Scalable Real-Time Data Systems”. IEEE Xplore. ISBN: 978-1-61327-034-3. https://ieeexplore.ieee.org/book/10279852

McQueen, B. (2017). “Big Data Analytics for Connected Vehicles and Smart Cities”. IEEE Xplore. ISBN: 9781630814748. https://ieeexplore.ieee.org/book/9100692

Chang, X., Li, H., Rong, J., Qin, L., & Zhao, X. (2021). "Spatiotemporal Characteristics of Vehicle Trajectories in a Connected Vehicle Environment—A Case of an Extra-Long Tunnel Scenario," IEEE Systems Journal, vol. 15, no. 2, pp. 2304-2315. https://ieeexplore.ieee.org/abstract/document/9095395 DOI: https://doi.org/10.1109/JSYST.2020.2990650

Vermesan, O., et al. (2021). "Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility," Frontiers in Future Transportation, vol. 2, no. 688482. https://www.frontiersin.org/journals/future-transportation/articles/10.3389/ffutr.2021.688482/full DOI: https://doi.org/10.3389/ffutr.2021.688482

Xu, Q., et al. (2022). "The status, challenges, and trends: an interpretation of technology roadmap of intelligent and connected vehicles in China (2020)," Journal of Intelligent and Connected Vehicles, vol. 5, no. 1, pp. 1-7. https://www.emerald.com/insight/content/doi/10.1108/jicv-07-2021-0010/full/html DOI: https://doi.org/10.1108/JICV-07-2021-0010

Zhu, T., et al. (2020). "A Survey of Predictive Maintenance: Systems, Purposes and Approaches," arXiv preprint arXiv:1912.07383. https://arxiv.org/abs/1912.07383

Giralda, D.B., et al. "Intelligent system for dynamic transport fleet management." IEEE Conference on Emerging Technologies and Factory Automation. https://ieeexplore.ieee.org/abstract/document/1612603

Natarajan, N., et al. (2022). "Optimization of Performance and Scalability Measures across Cloud-Based IoT Applications with Efficient Scheduling Approach," International Journal of Wireless Information Networks, vol. 29, no. 3, pp. 442-453. https://link.springer.com/article/10.1007/s10776-022-00568-5 DOI: https://doi.org/10.1007/s10776-022-00568-5