and mutation mechanisms. As such, the appliance of EA provides satisfactory solutions to NP-hard optimization issues. To boot, EA is additionally accustomed to solve several practical problems like finding associate degree optimum position for a bachelor’s degree in an exceedingly given space of interest. First State approach in simulation results. Associate degree illustrative example is shown within Figure 1.13. As the hoopla continues to grow concerning the 5G network, some folks forget it’s not excellent. In late 2018, a search cluster disclosed that though the 5G network has higher security compared to the 3G and 4G networks, numerous the vulnerabilities in those earlier versions got carried over to 5G, too. As an example, one drawback permits hackers to observe the final neck of the woods of a definite transportable on the 5G network. It’s unclear; however, those problems could influence IoT devices. Maybe AI could get the image if an organization develops Associate in Nursing algorithmic rule that may spot the hacking tries that exploit that vulnerability. But the lesson learned here is that folks shouldn’t anticipate that the 5G network is foolproof regarding security. Specialists already recognize that issues exist. The queries that stay are whether the 5G protocol can get improved, and if not, what adverse impact can the problems have?
As shown in Figures 1.13 and 1.14, the authors write up an algorithm that offered the changed RGA methodology for allocating the best positions of the long run 5G base stations. Figure 1.15 indicates the working example of transmission algorithm mentioned in Figure 1.14. The changed RGA has achieved sufficiently higher performance in terms of transmitting power-saving and total connected users for 5G networks with providing the best coverage. The changed RGA has found success with the tidy higher configuration by scrutinizing with standard Delaware and RGA to find the correct location and to adjust the varying ability level further to coverage constraints. In the current and future work they are going to study the best BSs (Base Stations) and their price relating to frequency level victimization a complicated Semitic deity. We are going to conjointly investigate the written record evolution of the energy within the normal of satisfactory QoS (Quality of Services).
Figure 1.13 Illustrative example ofthe proposed algorithm [41].
Figure 1.14 The algorithm for the transmission [41].
Figure 1.15 An illustrative example of the working of the transmission algorithm [41].
1.6 Conclusion & Future Work
In today’s era, almost everything is connected through the Internet. IoT devices network is a prominent example of it, and it leads to the requirement of faster communication network such as 5G. With faster communication, we can yield full capabilities of IoT devices in the various application domains such as healthcare, I-IoT, agriculture, etc. Presently some issues exist such as transmission efficiency and cell power utilization with limited solutions for these issues. In future more efficient algorithms, protocols and viable solution are required and will be developed to get the maximum potential of IoT devices with the 5G network.
References
1. Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M., Internet of Things (IoT): A vision, architectural elements, and future directions.
2. Ermolov, V. et al., Significance of Nanotechnology for future wireless devices and Communications. The 18th Annual IEEE International Symposium on PIMRC’07.
3. Salahuddin, M. A., Al-Fuqaha, A., & Guizani, M., Exploiting context severity to achieve opportunistic service differentiation in vehicular Ad Hoc networks. IEEE transactions on vehicular technology, 63, 6, 2901–2915, 2013.
4. Yu, Z., Liang, Y., Xu, B. et al., Towards a smart campus with mobile social networking, in: Proceedings of the 4th IEEE Int’l Conference on Cyber, Physical and Social Computing (CPSCom), IEEE, Dalian, China, 2011.
5. Pearl, D. Journal of Higher Education Outreach and Engagement, 17, 3, 1–157, 2013.
6. Minoli, D., and Occhiogrosso, B., Practical aspects for the integration of 5G networks and IoT applications in smart cities environments. Wireless Communications and Mobile Computing, 2019, 2019.
7. Painter, S., Fournier, J., Grape, C., Grummon, P., Morelli, J., Whitmer, S., & Cevetello, J., Research on learning space design: Present state, future directions. Society of College and University Planning, 2013.
8. Kyriazis, D., Varvarigou, T., White, D. et al., Sustainable smart city IoT applications: heat and electricity management & ecoconscious cruise control for public transportation, in: Proceedings of the IEEE 14th International Symposium on “A World of Wireless, Mobile and Multimedia Networks” (WoWMoM), IEEE, Madrid, Spain, 2013.
9. Minoli, D., What is the internet of things?. Building the Internet of Things with IPv6 and MIPv6: The Evolving World of M2M Communications, 1–27, 2013.
10. Grodi, R., Rawat, D.B., Rios-Gutierrez, F., Smart parking: Parking occupancy monitoring and visualization system for 28 Wireless Communications and Mobile Computing smart cities, in: Proceedings of the Southeast Con, IEEE, Norfolk, VA, USA, 2016.
11. Soudani, A., Marrón, P., & Shih, C. Y. Wireless Ad Hoc Sensor Networks.
12. Shuja, J., Gani, A., Shamshirband, S., Ahmad, R. W., & Bilal, K., Sustainable cloud data centers: A survey of enabling techniques and technologies. Renewable and Sustainable Energy Reviews, 62, 195–214, 2016.
13. Liu, X. F., Zhan, Z. H., Deng, J. D., Li, Y., Gu, T., & Zhang, J. An energy efficient ant colony system for virtual machine placement in cloud computing. IEEE transactions on evolutionary computation, 22, 1, 113–128, 2016.
14. Juels, A., RFID security and privacy: A research survey. IEEE journal on selected areas in communications, 24, 2, 381–394, 2006.
15. Ly, F., Carrier Network Virtualization 2016: The Future of 5G and System Integrators. Radisy, 2016.
16. Mahmoud, M. S., & Mohamad, A. A., A study of efficient power consumption wireless communication techniques/modules for internet of things (IoT) applications, 2016.
17. Lee, J.-S., Su, Y.-W., Shen, C.-C., A Comparative Study of Wireless Protocols: Bluetooth, UWB, ZigBee, and Wi-Fi. 33rd Annual Conference of the IEEE Industrial Electronics Society (IECON), Taipei, 5–8 November 2007, pp. 46–51, http://dx.doi.org/10.1109/iecon.2007.4460126.
18. Tozlu, S., Senel, M., Mao, W., Keshavarzain, A., Wi-Fi Enabled Sensors for Internet of Things: A Practical Approach. IEEE Commun. Mag., 50, 134–143, http://dx.doi.org/10.1109/MCOM.2012.6211498, 2012.
19. Burchfield, T.R., Venkatesan, S., Weiner, D., Maximizing Throughput in ZigBee Wireless Networks through Analysis, Simulations and Implementations. Proceedings of the International Workshop on Localized Algorithms and Protocols for Wireless Sensor Networks, Santa Fe, 18–20, 18–20 June 2007.
20. Alliance, L., LPWA technologies unlock new IoT market potential. White Paper. LoRa, 2015.
21. Nguyen, D. D., Nguyen, H. X., & White, L. B., Reinforcement learning with network-assisted feedback for heterogeneous RAT selection. IEEE Transactions on Wireless Communications, 16, 9, 6062–6076, 2017.
22. Le, Q. V., Jaitly, N., & Hinton, G. E., A simple way to initialize recurrent networks of rectified linear units.