Cloud-Integrated Framework for Enhancing Automotive Manufacturing and Embedded Software with Software-Defined Systems and IoT Traffic Optimization
Author : Venkata Surya Bhavana Harish Gollavilli and Aravindhan Kurunthachalam
Abstract :
This paper proposes a novel framework for adapting automotive manufacturing and embedded software to global market shifts, integrating cloud-based software-defined systems. With the rapid evolution of IoT and cloud technologies, the automotive industry faces the challenge of evolving legacy systems while maintaining operational efficiency and security. By leveraging data-driven approaches, the framework optimizes communication and operational performance through the analysis of IoT network traffic. The IoT Network Traffic Dataset serves as the foundation for this analysis, offering valuable insights into traffic patterns, security vulnerabilities, and system bottlenecks. The proposed framework enhances embedded software's adaptability and scalability, improving performance in a rapidly changing global market. Experimental results demonstrate the framework's effectiveness, with performance improvements of up to 20% in network efficiency, 15% in resource optimization, and 25% in security. The results are compared with traditional methods, showcasing significant advancements in system adaptability and operational efficiency. This work contributes to the field by offering a cloud-integrated, data-driven approach for adapting automotive manufacturing and embedded software to market shifts, providing a scalable solution for the future of the automotive industry.
Keywords :
Automotive Manufacturing, Embedded Software, IoT Network Traffic, Cloud Integration, Software-Defined Systems.
