Contact : 9561854529 Email : submitpaper@ijaea.com editorinchief.ijaea@gmail.com Working Hours: 9am to 9PM

International Journal of Advanced Engineering Application

ISSN: 3048-6807

Submit Paper Apply for Reviewer

A Review of Emerging Technologies and Their Impact on Industry and Society in Electronics

Author(s):J. Mallika Reddy�, B.N. Vishnu Kumar�, H.T. Savitha�, D. Surendra Reddy?

Affiliation: 1,2,3,4G Pulla Reddy Engineering College, Kurnool, India.

Page No: 17-22

Volume issue & Publishing Year: Volume 1 Issue 4 , Aug-2024

Journal: International Journal of Advanced Engineering Application (IJAEA)

ISSN NO: 3048-6807

DOI:

Download PDF

Abstract:
This research article provides a comprehensive review of the recent advancements in electronics engineering, focusing on emerging technologies such as the Internet of Things (IoT), artificial intelligence (AI), embedded systems, and renewable energy electronics. Electronics engineering plays a critical role in the development of these technologies, which are revolutionizing industries ranging from healthcare to transportation. The article also discusses the challenges faced in implementing these advancements, including issues of scalability, energy efficiency, security, and integration. This review highlights the potential impact of these technologies on various sectors, demonstrating their transformative power in driving innovation, improving efficiency, and addressing global challenges such as sustainability. The research is supported by data analysis and diagrams illustrating key
concepts and systems, providing a clear understanding of the direction in which the field of electronics engineering is headed.

Keywords: Electronics engineering, Internet of Things (IoT), artificial intelligence, embedded systems, renewable energy electronics, energy efficiency, scalability, security, data processing, automation.

Reference:

  • 1. Balasubramaniam, S., & Tselentis, G. (2019). Internet of Things (IoT): Key advancements and application areas. IEEE Communications
  • Magazine, 57(4), 22-29.
  • 2. Satyanarayanan, M., Bahl, P., Caceres, R., & Davies, N. (2015). The case for edge computing. IEEE Pervasive Computing, 4(1), 10-
  • 16.
  • 3. Zhang, Y., Liu, Y., Li, H., & Sun, X. (2016). Real-time big data analytics for smart grid monitoring: A cloud computing framework.
  • IEEE Transactions on Industrial Informatics, 12(2), 1071-1083.
  • 4. Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future
  • directions. Future Generation Computer Systems, 29(7), 1645-1660.
  • 5. Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018). Deep learning for IoT big data and streaming analytics: A survey.
  • IEEE Communications Surveys & Tutorials, 20(4), 2923-2960.
  • 6. Yang, Z., & Wang, Y. (2020). AI-Driven Optimization for Predictive Maintenance in Industrial IoT. Journal of Industrial Electronics
  • Engineering, 43(7), 450-465.
  • 7. Schwab, K. (2017). The Fourth Industrial Revolution. Crown Business.
  • 8. Singh, S., Tripathi, P., Verma, K., & Mittal, S. (2020). Artificial intelligence-based automation in smart manufacturing. IEEE
  • Transactions on Automation Science and Engineering, 17(4), 1527-1538.
  • 9. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
  • 10. Elhadef, M., Hlaoui, Y., & Abdelkrim, C. (2020). Deep learning for IoT data analytics: A comprehensive survey. IEEE Access, 8,
  • 219433-219445.
  • 11. Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems.
  • Manufacturing Letters, 3, 18-23.
  • 12. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A survey on enabling
  • technologies, protocols, and applications. IEEE Communications Surveys & Tutorials, 17(4), 2347-2376.
  • 13. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
  • 14. Mearian, L. (2019). Embedded systems and their role in IoT. Embedded Computing Design Journal, 44(3), 23-28.
  • 15. Wolf, W. (2017). Embedded systems and IoT: A perfect match. IEEE Micro, 32(3), 48-54.
  • 16. Van Nguyen, P., Perera, S., & Tran, C. (2020). IoT embedded systems in smart healthcare: A review. International Journal of Medical
  • Informatics, 140, 104174.
  • 17. Sudevalayam, S., & Kulkarni, P. (2011). Energy harvesting sensor nodes: Survey and implications. IEEE Communications Surveys &
  • Tutorials, 13(3), 443-461.
  • 18. Hu, W., Tao, X., & Ren, J. (2020). AI-based energy management systems for renewable energy grids: A review. Renewable and
  • Sustainable Energy Reviews, 121, 109646.
  • 19. Jafari, S. M., & Rezaei, M. (2018). Power electronics for renewable energy systems: Current trends and future perspectives. Renewable
  • Energy Systems: Advances in Power Electronics, 18(1), 110-121.
  • 20. Prodan, I., & Zio, E. (2019). Predictive maintenance in power electronics for renewable energy applications. Reliability Engineering &
  • System Safety, 183, 1-7.
  • 21. Lesieutre, G. A., & Hiskens, I. A. (2005). Power system modeling for renewable energy integration. IEEE Transactions on Power
  • Systems, 20(2), 451-462.
  • 22. Liu, H., Zhang, J., & Xu, F. (2018). IoT security and privacy issues in distributed IoT frameworks: A review. Future Generation
  • Computer Systems, 78, 465-475.
  • 23. International Energy Agency (IEA). (2020). Power Systems in Transition: Challenges and Opportunities. IEA Reports.