Assessing the Influence of 5G Technology on Distributed System Architectures for Real-Time Applications
Downloads
With the development of 5G technology, the distributed system architecture entirely changes from time division to space division, bringing ultra-low latency, enhanced scalability, improved reliability and becomes the basic infrastructure for real-time applications. This paper evaluates the effects of 5G technology on distinct distributed systems in various areas such as self-driving vehicles, smart cities, and smart industries. The study indicates that 5G enables the transmission of data in real-time and creates lasting end-to-end connections for the IoT of things, along with better reliability for important application systems. Moreover, the highlight of the sophisticated architectural en route to the current 5G realm, including edge computing and network slicing, also shows how distributed systems are flexible in utilizing 5G’s advantages. As much as 5G is seen to carry great potential, issues like high cost of deployment, security issues and problems within system integration require proper consideration to harness on 5G fully. On this, this research offers information about the role played by 5G on the future of the distributed systems and its relation to real-time systems.
Downloads
1. A. Rakita, N. Nikolić, M. Mildner, J. Matiasek, and A. Elbe-Bürger, “Re-epithelialization and immune cell behaviour in an ex vivo human skin model,” Scientific Reports, vol. 10, no. 1, p. 1, Jan. 2020, doi: https://doi.org/10.1038/s41598-019-56847-4
2. R. W. Heath, N. Gonzalez-Prelcic, S. Rangan, W. Roh, and A. M. Sayeed, “An Overview of Signal Processing Techniques for Millimeter Wave MIMO Systems,” IEEE Journal of Selected Topics in Signal Processing, vol. 10, no. 3, pp. 436–453, Apr. 2016, doi: https://doi.org/10.1109/jstsp.2016.2523924
3. R. Satija, J. A. Farrell, D. Gennert, A. F. Schier, and A. Regev, “Spatial reconstruction of single-cell gene expression data,” Nature Biotechnology, vol. 33, no. 5, pp. 495–502, Apr. 2015, doi: https://doi.org/10.1038/nbt.3192
4. Z. Zhou, X. Chen, E. Li, L. Zeng, K. Luo, and J. Zhang, “Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing,” Proceedings of the IEEE, vol. 107, no. 8, pp. 1738–1762, Aug. 2019, doi: https://doi.org/10.1109/jproc.2019.2918951
5. A. Keränen, J. Ott, and T. Kärkkäinen, “The ONE simulator for DTN protocol evaluation,” Proceedings of the Second International ICST Conference on Simulation Tools and Techniques, 2009, doi: https://doi.org/10.4108/icst.simutools2009.5674
6. S. S. Hubbard, C. Varadharajan, Y. Wu, H. Wainwright, and D. Dwivedi, “Emerging technologies and radical collaboration to advance predictive understanding of watershed hydrobiogeochemistry,” Hydrological Processes, vol. 34, no. 15, pp. 3175–3182, Jun. 2020, doi: https://doi.org/10.1002/hyp.13807
7. M. Simsek, A. Aijaz, M. Dohler, J. Sachs, and G. Fettweis, “5G-Enabled Tactile Internet,” IEEE Journal on Selected Areas in Communications, vol. 34, no. 3, pp. 460–473, Mar. 2016, doi: https://doi.org/10.1109/jsac.2016.2525398
8. H. Tataria, M. Shafi, A. F. Molisch, M. Dohler, H. Sjoland, and F. Tufvesson, “6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities,” Proceedings of the IEEE, vol. 109, no. 7, pp. 1166–1199, Jul. 2021, doi: https://doi.org/10.1109/jproc.2021.3061701
9. Felip Riera-Palou and Guillem Femenias, “Trade-offs in Cell-free Massive MIMO Networks: Precoding, Power Allocation and Scheduling,” Oct. 2019, doi: https://doi.org/10.1109/telsiks46999.2019.9002148
10. A. Yassin et al., “Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications,” IEEE Communications Surveys Tutorials, vol. 19, no. 2, pp. 1327–1346, 2017, doi: https://doi.org/10.1109/COMST.2016.2632427
11. I. F. Akyildiz, A. Kak, and S. Nie, “6G and Beyond: The Future of Wireless Communications Systems,” IEEE Access, vol. 8, pp. 133995–134030, 2020, doi: https://doi.org/10.1109/ACCESS.2020.3010896
12. A. Griciuc et al., “Alzheimer’s Disease Risk Gene CD33 Inhibits Microglial Uptake of Amyloid Beta,” Neuron, vol. 78, no. 4, pp. 631–643, May 2013, doi: https://doi.org/10.1016/j.neuron.2013.04.014
13. M. D. Robinson and G. K. Smyth, “Moderated statistical tests for assessing differences in tag abundance,” Bioinformatics, vol. 23, no. 21, pp. 2881–2887, Sep. 2007, doi: https://doi.org/10.1093/bioinformatics/btm453
14. R. Khan, P. Kumar, D. N. K. Jayakody, and M. Liyanage, “A Survey on Security and Privacy of 5G Technologies: Potential Solutions, Recent Advancements, and Future Directions,” IEEE Communications Surveys & Tutorials, vol. 22, no. 1, pp. 196–248, 2020, doi: https://doi.org/10.1109/comst.2019.2933899
15. C. Liu et al., “Transparent air filter for high-efficiency PM2.5 capture,” Nature Communications, vol. 6, no. 1, Feb. 2015, doi: https://doi.org/10.1038/ncomms7205
16. C. De Lima et al., “Convergent Communication, Sensing and Localization in 6G Systems: An Overview of Technologies, Opportunities and Challenges,” IEEE Access, vol. 9, pp. 26902–26925, 2021, doi: https://doi.org/10.1109/access.2021.3053486
17. A. Balador et al., “Wireless Communication Technologies for Safe Cooperative Cyber Physical Systems,” Sensors, vol. 18, no. 11, p. 4075, Nov. 2018, doi: https://doi.org/10.3390/s18114075
18. A. Capponi, C. Fiandrino, B. Kantarci, L. Foschini, D. Kliazovich, and P. Bouvry, “A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2419–2465, 2019, doi: https://doi.org/10.1109/comst.2019.2914030
19. P. Popovski et al., “Wireless Access in Ultra-Reliable Low-Latency Communication (URLLC),” IEEE Transactions on Communications, vol. 67, no. 8, pp. 5783–5801, Aug. 2019, doi: https://doi.org/10.1109/tcomm.2019.2914652
20. Gupta, A., & Jha, R. K. (2015). A survey of 5G network: Architecture and emerging technologies. IEEE access, 3, 1206-1232. 10.1109/ACCESS.2015.2461602
21. Din, S., Paul, A., & Rehman, A. (2019). 5G-enabled Hierarchical architecture for software-defined intelligent transportation system. Computer Networks, 150, 81-89.https://doi.org/10.1016/j.comnet.2018.11.035
22. Cosovic, M., Tsitsimelis, A., Vukobratovic, D., Matamoros, J., & Anton-Haro, C. (2017). 5G mobile cellular networks: Enabling distributed state estimation for smart grids. IEEE Communications Magazine, 55(10), 62-69.DOI: 10.1109/MCOM.2017.1700155
23. Cavalcanti, D., Perez-Ramirez, J., Rashid, M. M., Fang, J., Galeev, M., & Stanton, K. B. (2019). Extending accurate time distribution and timeliness capabilities over the air to enable future wireless industrial automation systems. Proceedings of the IEEE, 107(6), 1132-1152.DOI: 10.1109/JPROC.2019.2903414
24. Loghin, D., Cai, S., Chen, G., Dinh, T. T. A., Fan, F., Lin, Q., ... & Zhang, Z. (2020). The disruptions of 5G on data-driven technologies and applications. IEEE transactions on knowledge and data engineering, 32(6), 1179-1198.DOI: 10.1109/TKDE.2020.2967670
25. Garau, M., Anedda, M., Desogus, C., Ghiani, E., Murroni, M., & Celli, G. (2017, June). A 5G cellular technology for distributed monitoring and control in smart grid. In 2017 IEEE international symposium on broadband multimedia systems and broadcasting (BMSB) (pp. 1-6). IEEE.DOI: 10.1109/BMSB.2017.7986141
Copyright (c) 2023 Nagaraj Parvatha
This work is licensed under a Creative Commons Attribution 4.0 International License.
