Intelligent Connectivity. Abdulrahman Yarali. Читать онлайн. Newlib. NEWLIB.NET

Автор: Abdulrahman Yarali
Издательство: John Wiley & Sons Limited
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Жанр произведения: Программы
Год издания: 0
isbn: 9781119685210
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lowest individual level. This indicates a significant amount of consideration for evaluating the factors involved in what personalized services the individual might want at any instance, or at any given time for that matter (Dong et al. 2017). These challenges have been tackled in very different ways by the assistants' actual developers, and they have also addressed them in different ways. One of the major ideas is that a virtual assistant must become compatible with any device in proximity, which would allow any individual to control things that surround them. Therefore, the virtual assistants will need to be integrated with the most advanced capabilities in processing and actuating actions as and when the expectations should arise (Militano et al. 2015). However, the entire breadth of the necessary operations inherently reflects the requirements of very high requirements from communication capabilities, resulting in the adoption of 5G network connectivity. It is therefore quite apparent that virtual personal assistants will greatly undergo improvement over time, especially with intelligent connectivity.

      2.7.3.2 3D Hologram Displays

      It is quite an apparent fact that the entire cast of Intelligent Connectivity speaks greatly about the next‐generation communication capabilities to the greatest extent imaginable. However, one would be remiss if there was no mention whatsoever in terms of the advancements in processing and decision making it will bring forth. The point about computing technologies has long been about facilitating services that reflect minimum possible human intervention while also ensuring that there is enough personalized presentation, alongside a properly realized mode of complete cybersecurity at large. This is where AI will find its most consequential applications. There is also no doubt that the entire case of implementing AI brings forth a very high‐level requirement of a machine or deep learning, respectively. Looking at the AI as a self‐learning entity, it has become clear that addressing all of these factors represents challenges that are very hard to address and relate to. Moreover, the significant amounts of data required to be put into the entire field bring forth the overlying cybersecurity threat scenario reaching its definitive peak. This is the clear dilemma that sectors engaging in 5G connectivity, AI, and IoT must engage to the greatest extent imaginable.

      1 Abdelwahab, S., Hamdaoui, B., Guizani, M., and Znati, T. (2016). Network function virtualization in 5G. IEEE Communications Magazine 54 (4): 84–91.

      2 Akyildiz, I.F., Wang, P., and Lin, S.C. (2015). SoftAir: A software‐defined networking architecture for 5G wireless systems. Computer Networks 85: 1–18.

      3 Al‐Falahy, N. and Alani, O.Y. (2017). Technologies for 5G networks: Challenges and opportunities. IT Professional 19 (1): 12–20.

      4 Andrieu, C., De Freitas, N., Doucet, A., and Jordan, M.I. (2003). An introduction to MCMC for machine learning. Machine Learning 50 (1‐2): 5–43.

      5 Arel, I., Rose, D.C., and Karnowski, T.P. (2010). Deep machine learning – A new frontier in Artificial Intelligence research. IEEE Computational Intelligence Magazine 5 (4): 13–18.

      6 Chen, S. and Zhao, J. (2014). The requirements, challenges, and technologies for 5G of terrestrial mobile telecommunication. IEEE Communications Magazine 52 (5): 36–43.

      7 Dong, P., Zheng, T., Yu, S. et al. (2017). Enhancing vehicular communication using 5G‐enabled smart, collaborative networking. IEEE Wireless Communications 24 (6): 72–79.

      8 Duan, X. and Wang, X. (2015). Authentication handover and privacy protection in 5G Hetnets using software‐defined networking. IEEE Communications Magazine 53 (4): 28–35.

      9  Feng, W., Wang, J., Chen, Y. et al. (2018). UAV‐aided MIMO communications for 5G Internet of Things. IEEE Internet of Things Journal 6 (2): 1731–1740.

      10 French, A.M. and Shim, J.P. (2016). The digital revolution: Internet of Things, 5G, and beyond. Communications of the Association for Information Systems 38 (1): 40.

      11 Ge, X., Li, Z., and Li, S. (2017). 5G software‐defined vehicular networks. IEEE Communications Magazine 55 (7): 87–93.

      12 Ghahramani, Z. (2015). Probabilistic Machine Learning and Artificial Intelligence. Nature 521 (7553): 452–459.

      13 GSMA International (2018). New GSMA Report Highlights How 5G, Artificial Intelligence and IoT will Transform the Americas. https://www.gsma.com/newsroom/press‐release/new‐gsma‐report‐highlights‐how‐5g‐artificial‐intelligence‐and‐iot‐will‐transform‐the‐americas/ (accessed 23 April 2020).

      14 Hansen, J., Lucani, D.E., Krigslund, J. et al. (2015). Network coded software‐defined networking: Enabling 5G transmission and storage networks. IEEE Communications Magazine 53 (9): 100–107.

      15 Hassabis, D., Kumaran, D., Summerfield, C., and Botvinick, M. (2017). Neuroscience‐inspired Artificial Intelligence. Neuron 95 (2): 245–258.

      16 Ismail, N. (2019, January 15). Digital transformation in the telecom industry: what's driving it? Information Age: https://www.information‐age.com/digital‐transformation‐in‐the‐telecom‐industry‐123478152/ (accessed 22 June 2020).

      17 Jiang, F., Jiang, Y., Zhi, H. et al. (2017). Artificial Intelligence in healthcare: Past, present, and future. Stroke and vascular neurology 2 (4): 230–243.

      18 Katsaros, K. and Dianati, M. (e.) (2017). A conceptual 5G vehicular networking architecture. In: 5G Mobile Communications, 595–623. Cham: Springer.

      19 Kovac, M. and Leskova, A. (2012). Innovative applications of car connectivity network – Way to the intelligent vehicle. Journal of Systems Integration 3 (4): 51–60.

      20 Lemley, J., Bazrafkan, S., and Corcoran, P. (2017). Deep learning for consumer devices and services: Pushing the limits for Machine Learning, Artificial Intelligence, and Computer Vision. IEEE Consumer Electronics Magazine 6 (2): 48–56.

      21 Li, S., Da Xu, L., and Zhao, S. (2018). 5G Internet of Things: A survey. Journal of Industrial Information Integration 10: 1–9.

      22 Mahmood, K., Khan, M.A., Shah, A.M. et al. (2018). Intelligent on‐demand connectivity restoration for wireless sensor networks. Wireless Communications and Mobile Computing 2018, 1–10.

      23 Martini, B., Paganelli, F., Cappanera, P. et al. (2015, April). Latency‐aware composition of virtual functions in 5G. In: Proceedings of the 2015 1st IEEE Conference on Network Softwarization (NetSoft), 1–6. IEEE.

      24 Mellit, A., Kalogirou, S.A., Hontoria, L., and Shaari, S. (2009). Artificial Intelligence techniques for sizing photovoltaic systems: A review. Renewable and Sustainable Energy Reviews 13 (2): 406–419.

      25 Militano, L., Araniti, G., Condoluci, M. et al. (2015). Device‐to‐device communications for 5G Internet of Things. EAI Endorsed Trans Internet Things 1 (1):