Abstract
A smart city is defined as a one that provides solutions to rapid urbanization, exploding population, scarce resources, congested traffic, and energy management through the effective and integrated use of information and communication technology. The conceptualization, integration, and implementation of smart cities have been recognized and seen as a means to optimize the limited resources and improve the quality of human lives. The smart cities planning, designing, and development have been affected due to big data storage, big data governance, Internet of Things (IoT), and artificial intelligence (AI) techniques. The smart cities’ solutions cover different themes of varying importance such as smart health, smart education, intelligent transportation, smart energy, smart governance, etc. The emerging technologies are the one which are presently under development or might be developed in the future, and which can have a wide impact on research, business, and social lives. The emerging technologies are the groups of technologies that have been partially explored, continuously evolving, and under development such as, IoT, big data, machine learning (ML), social network, and cloud computing. The emerging technologies have created renewed interest in smart cities’ solutions. The smart cities’ progress and advancement are the results of the successful exploitation of emerging technologies.
This paper aims to investigate and discuss the success stories of emerging technologies in smart cities’ solutions. The emerging technologies included in the study are the IoT, big data, and AI. The paper further summarizes a process of applying tools and techniques for the successful initiative of transforming a traditional city into a smart one using emerging technologies.
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References
Lim, Y., Edelenbos, J., Gianoli, A.: Smart energy transition: an evaluation of cities in South Korea. Informatics 6, 50 (2019)
Manyika, J., Chui, M., Bisson, P., Woetzel, J.: The internet of things: mapping the value beyond the hype. McKinsey Global Institute (2015)
Khan, A.I., Al-Mulla, Y.: Unmanned aerial vehicle in the machine learning environment. Proc. Comput. Sci. 160, 46–53 (2019)
Jiang, D.: The construction of smart city information system based on the internet of things and cloud computing. Comput. Commun. 150, 158–166 (2020)
Khan, A.I., Al-Badi, A.: Open source machine learning frameworks for industrial internet of things. Proc. Comput. Sci. 170, 571–577 (2020)
Bibri, S.E.: The IoT for smart sustainable cities of the future: an analytical framework for sensor-based big data applications for environmental sustainability. Sustain. Cities Soc. 38, 230–253 (2018)
Al-Badi, A., Tarhini, A., Khan, A.I.: Exploring big data governance frameworks. Proc. Comput. Sci. 141, 271–277 (2018)
Wu, Y.C., Wu, Y.J., Wu, S.M.: An outlook of a future smart city in Taiwan from post – internet of things to artificial intelligence internet of things. In: Smart Cities: Issues and Challenges, pp. 263–282. Elsevier (2019)
Lim, C., Kim, K.-J., Maglio, P.P.: Smart cities with big data: reference models, challenges, and considerations. Cities 82, 86–99 (2018)
Khan, A.I., Al-Habsi, S.: Machine learning in computer vision. Proc. Comput. Sci. 167, 1444–1451 (2019). https://doi.org/10.1016/j.procs.2020.03.355
See, S.: Artificial intelligence computing for a smart city. In: Mehmood, R., Bhaduri, B., Katib, I., Chlamtac, I. (eds.) SCITA 2017. LNICST, vol. 224, pp. 6–8. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94180-6_2
Mohammadi, M., Al-Fuqaha, A., Sorour, S., Guizani, M.: Deep learning for IoT big data and streaming analytics: a survey. IEEE Commun. Surv. Tutor. 20, 2923–2960 (2018)
Zaouali, K., Rekik, R., Bouallegue, R.: Deep learning forecasting based on auto-LSTM model for home solar power systems. In: 20th International Conference on High Performance Computing and Communications, Smart City, Data Science and Systems, pp. 235–242. IEEE, Exeter (2018)
Liu, Z., et al.: A Bayesian approach to residential property valuation based on built environment and house characteristics. In: International Conference on Big Data, pp. 1455–1464. IEEE, Seattle (2018)
Preda, S., Oprea, S.-V., Bâra, A.: PV forecasting using support vector machine learning in a big data analytics context. Symmetry 10, 748 (2018)
Jules, T.D., Salajan, F.D.: The Educational Intelligent Economy: Big Data, Artificial Intelligence, Machine Learning and the Internet of Things in Education. Emerald Group Publishing, Bingley (2019)
Sadeh, A., Feniser, C., Dusa, S.I.: Technology education and learning in smart cities. In: Developing Technology Mediation in Learning Environments, pp. 78–95. IGI Global (2020)
Fakroon, M., Alshahrani, M., Gebali, F., Traore, I.: Secure remote anonymous user authentication scheme for smart home environment. Internet Things 9, 100–158 (2020)
Navarro, J.L.A., Ruiz, V.R.L., Peña, D.N.: The effect of ICT use and capability on knowledge-based cities. Cities 60, 272–280 (2017)
Muscat Media Group. https://timesofoman.com/article/48874/Oman/Omans-ITA–focuson-smart-cities
Conrad, P.: S Korea to support Oman’s smart city ambitions. Oman Daily Observer (2018)
Al-Mahrooqi, S.: Developing the most significant and suitable smart city indicators for smart city pilot in Knowledge Oasis Muscat (KOM), Sultanate of Oman. United Nations University (2019)
Igel, C., Ullrich, C., Kravcik, M.: Using artificial intelligence and the internet of things to enable context-dependent recommendations in the smart city and smart factory. Athens J. Sports 5, 253–262 (2018)
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Al-Badi, A., Sharma, S.K., Jain, V., Khan, A.I. (2020). Investigating Emerging Technologies Role in Smart Cities’ Solutions. In: Sharma, S.K., Dwivedi, Y.K., Metri, B., Rana, N.P. (eds) Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation. TDIT 2020. IFIP Advances in Information and Communication Technology, vol 618. Springer, Cham. https://doi.org/10.1007/978-3-030-64861-9_21
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DOI: https://doi.org/10.1007/978-3-030-64861-9_21
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