Embedded System Design for Automation in Industrial Processes and Smart Infrastructure Applications

Main Article Content

Rohit Trisha Kulkarni

Abstract

The integration of embedded systems in industrial automation and smart infrastructure is pivotal for advancing operational efficiency, reliability, and scalability. These systems facilitate real-time monitoring, control, and optimization of processes, contributing to the realization of Industry 4.0. This paper explores the design and implementation of embedded systems tailored for automation in industrial processes and smart infrastructure applications. We examine various embedded platforms, communication protocols, and control strategies employed to enhance system performance and adaptability. Case studies are presented to illustrate the practical applications and benefits of these systems in real-world scenarios. The findings highlight the significance of selecting appropriate hardware and software components to meet specific operational requirements, ensuring seamless integration and sustainable performance. Challenges such as system complexity, cybersecurity, and interoperability are also discussed, along with strategies to mitigate these issues. The paper concludes with recommendations for future research directions to further advance embedded system technologies in industrial automation and smart infrastructure domains.MDPI

Article Details

Section

Articles

How to Cite

Embedded System Design for Automation in Industrial Processes and Smart Infrastructure Applications. (2021). International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(1), 4334 - 4336. https://doi.org/10.15662/IJRPETM.2021.0501001

References

1. Lesi, V., Conti, M., & Spenza, D. (2020). Security Challenges in Distributed IoT-based Industrial Automation

Systems. arXiv preprint arXiv:2006.00044.

2. Viola, F., & Chen, Y. (2020). Digital Twin-enabled Smart Control Engineering: Real-time Temperature Uniformity

Control for Smart Manufacturing. arXiv preprint arXiv:2007.03677.

3. Khalil, M., Djemame, K., & Sellami, A. (2020). Deep Learning in Industrial Internet of Things: Potentials and

Challenges. arXiv preprint arXiv:2008.06701.

4. Lee, J., Bagheri, B., & Jin, C. (2018). Introduction to Cyber Manufacturing. Manufacturing Letters, 15, 1-4.

5. Wolf, W. (2020). Computers as Components: Principles of Embedded Computing System Design. Morgan

Kaufmann.

6. Janakiraman, R., et al. (2020). Embedded Systems for Smart Infrastructure: Challenges and Solutions. IEEE Access,

8, 118754-118765.