Autonomous Cloud-Native Ingestion of High-Frequency MQTT Telemetry for  Predictive Anomaly Intelligence in Next-Generation Automotive Powertrain  Manufacturing

Yogesh Pugazhendhi Duraisamy Rajamani

Autonomous Cloud-Native Ingestion of High-Frequency MQTT Telemetry for Predictive Anomaly Intelligence in Next-Generation Automotive Powertrain Manufacturing

Authors

Yogesh Pugazhendhi Duraisamy Rajamani

Keywords:

MQTT ingestion, predictive anomaly detection, cloud-native streaming, automotive manufacturing, real-time telemetry

Abstract

Modern powertrain manufacturing environments generate enormous amounts of high-frequency telemetrydata from engines and transmissions that require real-time streaming analytics and scalable storage. Acloud-native analytics platform that supports the continuous ingestion of MQTT-based streaming powertrain telemetry data from manufacturing

References

Kamran Sattar Awaisi, et al., "A Survey of Industrial AIoT: Opportunities, Challenges, and Directions," IEEE Internet of Things Journal, 10 July 2024. [Online]. Available: https://ieeexplore.ieee.org/document/10591982

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Published

2026-01-31

How to Cite

Yogesh Pugazhendhi Duraisamy Rajamani. (2026). Autonomous Cloud-Native Ingestion of High-Frequency MQTT Telemetry for Predictive Anomaly Intelligence in Next-Generation Automotive Powertrain Manufacturing. Journal of Computational Analysis and Applications (JoCAAA), 35(1), 777–791. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/4794