Multifaceted Interplay between Mobile Edge Computing based on Industry 5.0 in Transportation

Authors

  • Salar Faisal Noori Department of Computer Science, Cihan University-Duhok, Duhok, Iraq
  • D. Yuvaraj Department of Computer Science, Cihan University-Duhok, Duhok, Iraq
  • Shakir Mahoomed Abas Department of Computer Science, Cihan University-Duhok, Duhok, Iraq
  • M. Sivaram Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India
  • V. Porkodi Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India

Keywords:

Multi-access edge computing, IoT, Cloud computing, Transportation, Automobiles

Abstract

A new technology called mobile edge computing, or MEC, is now acknowledged as a crucial 5G network enabler. The demand for computation-intensive mobile network applications—which call for greater storage, potent machines, and real-time responses—has increased significantly in recent years. Because they must support many services, including traffic monitoring or data sharing involving various aspects of vehicular traffic, transportation systems play a crucial part in this ecosystem. Furthermore, new resource-hungry applications like in-car entertainment and self-driving cars have been imagined, making the need for processing and storage resources one of the biggest problems facing transportation networks. With the advent of multi-access edge computing (MEC) technological advances, real-time, high-bandwidth, minimal latency access to radio network resources is intended to be made possible by bringing cloud computing capabilities to the edge of the wireless access network. With MEC's capacity to offer cloud computing and gateways capabilities at the network edge, IoT is recognized as a major application case for the technology. Because of its extensive mobility support and dense geographical spread, MEC will stimulate the development of a wide range of apps and services that require ultralow latencies and high quality of service. For this reason, MEC is a crucial enabler of Internet of Things services and applications that need immediate operation. At last, the globally ideal answer has been achieved. The suggested strategy is superior, as shown by the simulation results.

Downloads

Download data is not yet available.

References

Porambage, P., Okwuibe, J., Liyanage, M., Ylianttila, M., & Taleb, T. (2018). Survey on multi-access edge computing for internet of things realization. IEEE Communications Surveys & Tutorials, 20(4), 2961-2991.

Abbas, N., Zhang, Y., Taherkordi, A., & Skeie, T. (2017). Mobile edge computing: A survey. IEEE Internet of Things Journal, 5(1), 450-465.

Fraga-Lamas, P., Lopes, S. I., & Fernández-Caramés, T. M. (2021). Green IoT and edge AI as key technological enablers for a sustainable digital transition towards a smart circular economy: An industry 5.0 use case. Sensors, 21(17), 5745.

Nauman, A., Khan, W. U., Aldehim, G., Alqahtani, H., Alruwais, N., Al Duhayyim, M., ... & Nkenyereye, L. (2023). Communication and computational resource optimization for Industry 5.0 smart devices empowered by MEC. Journal of King Saud University-Computer and Information Sciences, 101870.

Fraga-Lamas, P., Barros, D., Lopes, S. I., & Fernández-Caramés, T. M. (2022). Mist and edge computing cyber-physical human-centered systems for industry 5.0: A cost-effective IoT thermal imaging safety system. Sensors, 22(21), 8500.

Aljubayrin, S., Aldehim, G., Alruwais, N., Mahmood, K., Al Duhayyim, M., Min, H., ... & Khan, W. U. (2023). Dynamic offloading strategy for computational energy efficiency of wireless power transfer based MEC networks in industry 5.0. Journal of King Saud University-Computer and Information Sciences, 35(10), 101841.

Taj, I., & Zaman, N. (2022). Towards industrial revolution 5.0 and explainable artificial intelligence: Challenges and opportunities. International Journal of Computing and Digital Systems, 12(1), 295-320.

Liu, J., Pan, B., Zhang, X., & Li, D. (2021). Mobile E-commerce information system based on industry cluster under edge computing. Mobile Information Systems, 2021, 1-11.

Wang, A., & Yi, X. (2021). Attitude Perception of Badminton Players Based on Mobile Edge Computing. Scientific Programming, 2021, 1-12.

Jiao, T. (2021). Mobile English teaching information service platform based on edge computing. Mobile Information Systems, 2021, 1-10.

Gao, D. (2022). Computing resource allocation strategy based on mobile edge computing in internet of vehicles environment. Mobile Information Systems, 2022.

Bajic, B., Suzic, N., Moraca, S., Stefanović, M., Jovicic, M., & Rikalovic, A. (2023). Edge Computing Data Optimization for Smart Quality Management: Industry 5.0 Perspective. Sustainability, 15(7), 6032.

Akundi, A., Euresti, D., Luna, S., Ankobiah, W., Lopes, A., & Edinbarough, I. (2022). State of Industry 5.0—Analysis and identification of current research trends. Applied System Innovation, 5(1), 27.

Ahmad, A. Y. B., Gongada, T. N., Shrivastava, G., Gabbi, R. S., Islam, S., & Nagaraju, K. (2023). E-commerce trend analysis and management for Industry 5.0 using user data analysis. International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 135-150.

Kumar, V. A., Kumar, A., Batth, R. S., Rashid, M., Gupta, S. K., & Raghuraman, M. (2021). Efficient data transfer in edge envisioned environment using artificial intelligence based edge node algorithm. Transactions on Emerging Telecommunications Technologies, 32(6), e4110.

S.V.Manikanthan, Padmapriya.T, “RECENT TRENDS IN M2M COMMUNICATIONS IN 4G NETWORKS AND EVOLUTION TOWARDS 5G”, International Journal of Pure and Applied Mathematics, Vol. 115, No. 8, pp: 623-630, 2017.

Downloads

Published

07.02.2024

How to Cite

Noori, S. F. ., Yuvaraj, D. ., Abas, S. M. ., Sivaram, M. ., & Porkodi, V. . (2024). Multifaceted Interplay between Mobile Edge Computing based on Industry 5.0 in Transportation. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 106–114. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4721

Issue

Vol. 12 No. 15s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.

Most read articles by the same author(s)