BOOSTING 5G NETWORK EDGE COMPUTING TO REDUCE LATENCY AND IMPROVE RESPONSE TIMES

Authors

  • Nabil Abdulwahab Abdulrazaq Baban PhD in Electrical Engineering, Computer Engineering Techniques Department, Al-Nukhba University College

DOI:

https://doi.org/10.30572/2018/KJE/170211

Keywords:

Edge computing, 5G mobile communication, Cloud computing, Real-time systems, Servers, Quality of service

Abstract

The rapid deployment of 5G networks promises ultra-low latency and high-speed communication, yet meeting the stringent requirements of latency-sensitive applications remains a challenge. Traditional cloud computing architectures struggle to deliver real-time processing due to the physical distance between data centers and end-users. This paper addresses the problem of reducing latency in 5G networks by enhancing edge computing capabilities. Our contribution lies in developing an optimized edge computing framework that dynamically allocates resources and offloads tasks to the network edge, thereby reducing response times. While previous works focus primarily on cloud-based solutions or static edge configurations, this paper introduces a hybrid approach that combines adaptive multi-tier edge orchestration with blockchain coordination (AMTEO-BC) for enhanced security and reliability by integrating edge computing with machine learning models to predict and adapt to real-time network condition cases to achieves faster data processing, reduced network congestion, and improved overall system performance. By leveraging advanced techniques such as distributed caching, task offloading, and intelligent resource allocation, we aim to bring computation and storage closer to the network edge, thereby minimizing the distance data travel. Simulation results demonstrate a significant reduction in latency compared to existing methods, offering a practical solution for time-critical 5G applications such as autonomous vehicles and real-time analytics. The Hierarchical Distributed Edge Computing Model (HDEC) could be a game-changer in reducing latency in 5G edge computing, especially for applications that require near-instantaneous response times

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References

Abbas, N., Zhang, Y., Taherkordi, A., and Skeie, T. (2017). Mobile edge computing: A survey. IEEE Internet of Things Journal, 5(1), 450-465. https://people.computing.clemson.edu/~jmarty/projects/lowLatencyNetworking/papers/RecentEdgeML-5GMEC/MobileEdgeComputing-Survey.pdf.

Al-Dulaimy, A., Sharma, Y., Khan, M. G., and Taheri, J. (2020). Introduction to edge computing. Edge Computing: Models, technologies and applications, 3-25. https://www.researchgate.net/publication/344218125_Introduction_to_edge_computing.

Bonomi, F., Milito, R., Natarajan, P., and Zhu, J. (2014). Fog computing: A platform for internet of things and analytics. Big data and internet of things: A roadmap for smart environments, 169-186. https://www.researchgate.net/publication/260753114.

Cao, Y., Chen, S., Hou, P., and Brown, D. (2015). FAST: A fog computing assisted distributed analytics system to monitor fall for stroke mitigation. In 2015 IEEE international conference on networking, architecture and storage (NAS) (pp. 2-11). IEEE. https://ieeexplore.ieee.org/document/7255196.

Chiang, M. and Zhang, T. (2016). Fog and IoT: An overview of research opportunities. IEEE Internet of things journal, 3(6), pp.854-864. 10.1109/JIOT.2016.2584538. 3. pp 854 – 864. https://ieeexplore.ieee.org/document/7498684

Chiang, M., and Zhang, T. (2016). Fog and IoT: An overview of research opportunities. IEEE Internet of things journal, 3(6), 854-864. https://ieeexplore.ieee.org/document/7498684.

Cisco Annual Internet Report (2023). Global Mobile Data Traffic Forecast Update (2018–2023). Cisco Systems. Inc. https://www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.html.

Durisi, G., Koch, T., and Popovski, P. (2016). Toward massive, ultrareliable, and low-latency wireless communication with short packets. Proceedings of the IEEE, 104(9), 1711-1726.

ETSI (2019). Multi-access Edge Computing (MEC); Framework and Reference Architecture. ETSI GS MEC 003. https://www.scribd.com/document/522784055/gs-MEC003v020201p.

Gao, Q., Xiao, J., Cao, Y., Deng, S., Ouyang, C., and Feng, Z. (2023). Blockchain-based collaborative edge computing: efficiency, incentive and trust. Journal of Cloud Computing, 12(1), 72. https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-023-00452-4.

Hu, Y. C., Patel, M., Sabella, D., Sprecher, N., and Young, V. (2015). Mobile edge computing—A key technology towards 5G. ETSI white paper, 11(11), 1-16. https://docslib.org/doc/612752/mobile-edge-computing-a-key-technology-towards-5g.

Ji, T., Luo, C., Yu, L., Wang, Q., Chen, S., Thapa, A., and Li, P. (2022). Energy-efficient computation offloading in mobile edge computing systems with uncertainties. IEEE Transactions on Wireless Communications, 21(8), 5717-5729. https://arxiv.org/pdf/2201.10398.

Li, X., and Simon, G. (2015). Content Delivery Networks: Status. Trends. and Future. IEEE Communications Magazine. 53. pp 40-46.

Liu, Q., Wang, Z., and Li, J (2022). Reinforcement learning-based task offloading for 5G edge computing. IEEE Transactions on Vehicular Technology. 69. pp 3626-3637. https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-022-00352-z.

Mach, P., and Becvar, Z. (2017). Mobile edge computing: A survey on architecture and computation offloading. IEEE communications surveys and tutorials, 19(3), 1628-1656. https://www.scribd.com/document/740922514/.

Mao, Y., You, C., Zhang, J., Huang, K., and Letaief, K. B. (2017a). A survey on mobile edge computing: The communication perspective. IEEE communications surveys and tutorials, 19(4), 2322-2358. https://export.arxiv.org/abs/1701.01090v4.

Mao, Y., Zhang, J., and Letaief, K. (2017b). Mobile Edge Computing: The Key Technology Towards 5G. IEEE Vehicular Technology Magazine. 12. pp 53-59. https://ieeexplore.ieee.org/document/8016573.

Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. Satoshi Nakamoto Institute. https://nakamotoinstitute.org/library/bitcoin/

Osseiran, A., Boccardi, F., Braun, V., Kusume, K., Marsch, P., Maternia, M., Queseth, O., Schellmann, M., Schotten, H., Taoka, H. and Tullberg, H. (2014). Scenarios for 5G mobile and wireless communications: the vision of the METIS project. IEEE communications magazine, 52(5), pp.26-35. https://www.researchgate.net/profile/Afif-Osseiran/publication/262416967.

Papadakis-Vlachopapadopoulos, K., Dimolitsas, I., Dechouniotis, D., Tsiropoulou, E. E., Roussaki, I., and Papavassiliou, S. (2021). On blockchain-based cross-service communication and resource orchestration on edge clouds. In Informatics (Vol. 8, No. 1, p. 13). MDPI. https://doi.org/10.3390/informatics8010013.

Porambage, P., Okwuibe, J., Liyanage, M., Ylianttila, M., and Taleb, T. (2018). Survey on multi-access edge computing for internet of things realization. IEEE Communications Surveys and Tutorials, 20(4), 2961-2991. https://acris.aalto.fi/ws/portalfiles/portal/31285423/ELEC_Porambage_survey_on_Multi_access_edge_IEEECS.pdf.

Qiu, H., Zhang, X., and Yang, Y. (2023). Simulation and performance analysis of latency reduction in 5G edge computing. Journal of Network and Computer Applications. 120. 49-60. https://www.sciencedirect.com/science/article/abs/pii/S1352231022005118.

Rathi, V. K., Chaudhary, V., Rajput, N. K., Ahuja, B., Jaiswal, A. K., Gupta, D., and Hammoudeh, M. (2020). A blockchain-enabled multi domain edge computing orchestrator. IEEE Internet of Things Magazine, 3(2), 30-36.

Shi, W., Cao, J., Zhang, Q., Li, Y. and Xu, L. (2016). Edge computing: Vision and challenges. IEEE internet of things journal, 3(5), 637-646. https://www.researchgate.net/publication/303890546_Edge_Computing_Vision_and_Challenges

Singh A (2019). Edge Computing Simply in Depth. 2nd Edition. Publisher: Amazon LLC. USISBN: 978-1091335295. https://www.researchgate.net/publication/354403370_Edge_Computing_Simply_In_Depth

Smith, J. and Patel, L. (2020). Blockchain Integration in Edge Computing: Performance and Security Aspects. Journal of Blockchain and Edge Technology.

Song, J., Gu, T., and Mohapatra, P. (2021). How blockchain can help enhance the security and privacy in edge computing? In 2021 IEEE/ACM Symposium on Edge Computing (SEC) (pp. 448-453). IEEE. https://arxiv.org/abs/2111.00416

Stantchev, V., Barnawi, A., Ghulam, S., Schubert, J., and Tamm, G. (2014). Smart items, fog and cloud computing as enablers of servitization in healthcare. Sensors and Transducers, 185(2), 121-128. https://www.researchgate.net/publication/284430697.

Sutton M. J. Liu X. and Shen X. S. (2019). Edge Computing for 5G Networks: Challenges and Research Opportunities. IEEE Network, 33, 96-105.

Taleb, T., Samdanis, K., Mada, B., Flinck, H., Dutta, S., and Sabella, D. (2017). On multi-access edge computing: A survey of the emerging 5G network edge cloud architecture and orchestration. IEEE Communications Surveys and Tutorials, 19(3), 1657-1681. https://ieeexplore.ieee.org/abstract/document/7931566/citations#citations.

Vijayakumar, P., Rajalingam, P., and Rajeswari, S. V. K. R. (2021). Edge Computing Optimization Using Mathematical Modeling, Deep Learning Models, and Evolutionary Algorithms. Simulation and Analysis of Mathematical Methods in Real‐Time Engineering Applications, 17-44. https://doi.org/10.1002/9781119785521.ch2.

Wang, F., Xu, J., Wang, X., and Cui, S. (2017b). Joint offloading and computing optimization in wireless powered mobile-edge computing systems. IEEE transactions on wireless communications, 17(3), 1784-1797. https://ieeexplore.ieee.org/abstract/document/8234686.

Wang, K. (2023). Edge Computing for 5G Networks: Theoretical Foundations and Practical Solutions. Title of Textbook. 2nd ed.

Wang, L., Feng, Y., and Chen, G. (2023). A machine-learning-driven hybrid architecture for 5G edge computing. IEEE Transactions on Network and Service Management. 17. pp 653-665. https://dl.acm.org/doi/full/10.1145/3555802.

Wang, S., Urgaonkar, R., He, T., Chan, K., Zafer, M., and Leung, K. K. (2016). Dynamic service placement for mobile micro-clouds with predicted future costs. IEEE Transactions on Parallel and Distributed Systems, 28(4), 1002-1016. https://dl.acm.org/doi/10.1109/TPDS.2016.2604814.

Wang, S., Zhang, X., Zhang, Y., Wang, L., Yang, J., and Wang, W. (2017a). A survey on mobile edge networks: Convergence of computing, caching and communications. Ieee Access, 5, 6757-6779. https://ieeexplore.ieee.org/document/7883826.

Wang, X., Han, Y., Leung, V. C., Niyato, D., Yan, X. and Chen, X. (2020). Convergence of edge computing and deep learning: A comprehensive survey. IEEE Communications Surveys and Tutorials, 22(2), 869-904. https://arxiv.org/abs/1907.08349

Xu, L., Zhao, S., and Chen, H. (2021). Distributed caching in 5G edge computing: A deep learning approach. IEEE Access. 9. 17304-17314. https://link.springer.com/article/10.1007/s00500-021-06496-5.

Zhang, Y., Zhao, P., and Hu, W. (2023). Federated learning-based task offloading for privacy-preserving 5G edge computing. IEEE Wireless Communications. 27. 70-77. https://www.bing.com/videos/search?q=Zhang%2c+Y.%2c+Zhao%2c+P.%2c+%26+Hu%2c+W.+(2023).

Zhao, D. (2021). Energy-Efficient Task Offloading in Mobile Edge Computing for 5G Networks. IEEE Transactions on Wireless Communications.

Zhao, Y., Zhang, W., Zhou, L., and Cao, W. (2021). A survey on caching in mobile edge computing. Wireless Communications and Mobile Computing, 2021(1), 5565648. https://doi.org/10.1155/2021/5565648.

Zhu, J., Chan, D. S., Prabhu, M. S., Natarajan, P., Hu, H., and Bonomi, F. (2013). Improving web sites performance using edge servers in fog computing architecture. In 2013 IEEE Seventh International Symposium on Service-Oriented System Engineering (pp. 320-323). IEEE. https://ieeexplore.ieee.org/document/6525539.

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Published

2026-05-02

How to Cite

Baban, Nabil Abdulwahab Abdulrazaq. “BOOSTING 5G NETWORK EDGE COMPUTING TO REDUCE LATENCY AND IMPROVE RESPONSE TIMES”. Kufa Journal of Engineering, vol. 17, no. 2, May 2026, pp. 171-93, https://doi.org/10.30572/2018/KJE/170211.

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