The Role of Cloud Technologies in the Development of the Connected Car Ecosystem and the Internet of Things
Keywords:
connected car, Internet of Things, cloud computing, edge computing, fog computing, vehicle telemetry, cloud-native architecture, Google Cloud Platform, automotive software, software-defined vehicleAbstract
The article examines how cloud technologies shape the connected car ecosystem and its interaction with the Internet of Things. Relevance follows from the rapid growth of software-defined vehicles, continuous telemetry streams, and service models based on remote computation, storage, and lifecycle analytics. Novelty is associated with an integrated synthesis that links in-vehicle data acquisition, edge/fog mediation, and cloud-native backends to concrete engineering decisions in automotive software production, with emphasis on Google Cloud Platform–oriented stacks and Java-centric enterprise integration. The work aims to systematize architectural patterns that enable scalable ingestion, secure communication, fleet-level management, and data-driven services across vehicles, roadside infrastructure, and IoT platforms. The study applies analytical review, comparative reasoning, and structured source analysis to examine peer-reviewed research and provider architectures. The conclusion formulates technology implications for platform design, data governance, and deployment practices in automobile software, highlighting trade-offs among latency, resilience, cost, and security. The article targets researchers and practitioners in automotive IT, cloud engineering, and applied AI.
References
[1] Abdullah, M. F. A., Yogarayan, S., Abdul Razak, S. F., Azman, A., Muhamad Amin, A. H., & Salleh, M. (2023). Edge computing for vehicle to everything: A short review. F1000Research, 10, 1104. https://doi.org/10.12688/f1000research.73269.4
[2] Amazon Web Services. (2024, January 12). Designing next-generation vehicle communication with AWS IoT Core and MQTT [White paper]. https://docs.aws.amazon.com/pdfs/whitepapers/latest/designing-next-generation-vehicle-communication-aws-iot/designing-next-generation-vehicle-communication-aws-iot.pdf
[3] Behravan, K., Farzaneh, N., Jahanshahi, M., & Hosseini Seno, S. A. (2023). A comprehensive survey on using fog computing in vehicular networks. Vehicular Communications, 42, 100604. https://doi.org/10.1016/j.vehcom.2023.100604
[4] Google Cloud. (2024, September 9). Connected device architectures on Google Cloud. https://docs.cloud.google.com/architecture/connected-devices
[5] Ku, D., Zang, H., Yusupov, A., Park, S., & Kim, J. (2025). Vehicle-to-everything-car edge cloud management with development, security, and operations automation framework. Electronics, 14(3), 478. https://doi.org/10.3390/electronics14030478
[6] Keshari, N., Singh, D., & Maurya, A. K. (2022). A survey on vehicular fog computing: Current state-of-the-art and future directions. Vehicular Communications, 38, 100512. https://doi.org/10.1016/j.vehcom.2022.100512
[7] Lu, S., & Shi, W. (2023). Vehicle computing: Vision and challenges. Journal of Information and Intelligence, 1(1), 23–35. https://doi.org/10.1016/j.jiixd.2022.10.001
[8] Mostefaoui, A., Merzoug, M. A., Haroun, A., Nassar, A., & Dessables, F. (2022). Big data architecture for connected vehicles: Feedback and application examples from an automotive group. Future Generation Computer Systems, 134, 374–387. https://doi.org/10.1016/j.future.2022.04.020
[9] Rocha, D., Teixeira, G., Vieira, E., Almeida, J., & Ferreira, J. (2023). A modular in-vehicle C-ITS architecture for sensor data collection, vehicular communications and cloud connectivity. Sensors, 23(3), 1724. https://doi.org/10.3390/s23031724
[10] Zhu, L., Li, B., & Tan, L. (2024). Vehicular edge cloud computing content caching optimization solution based on content prediction and deep reinforcement learning. Ad Hoc Networks, 165, 103643. https://doi.org/10.1016/j.adhoc.2024.103643
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Madugula Abhiram
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who submit papers with this journal agree to the following terms.
