Cyberphysical Smart Cities Infrastructures

Здесь есть возможность читать онлайн «Cyberphysical Smart Cities Infrastructures» — ознакомительный отрывок электронной книги совершенно бесплатно, а после прочтения отрывка купить полную версию. В некоторых случаях можно слушать аудио, скачать через торрент в формате fb2 и присутствует краткое содержание. Жанр: unrecognised, на английском языке. Описание произведения, (предисловие) а так же отзывы посетителей доступны на портале библиотеки ЛибКат.

Cyberphysical Smart Cities Infrastructures: краткое содержание, описание и аннотация

Предлагаем к чтению аннотацию, описание, краткое содержание или предисловие (зависит от того, что написал сам автор книги «Cyberphysical Smart Cities Infrastructures»). Если вы не нашли необходимую информацию о книге — напишите в комментариях, мы постараемся отыскать её.

Learn to deploy novel algorithms to improve and secure smart city infrastructure
Cyberphysical Smart Cities Infrastructures: Optimal Operation and Intelligent Decision Making,
Cyberphysical Smart Cities Infrastructures

Cyberphysical Smart Cities Infrastructures — читать онлайн ознакомительный отрывок

Ниже представлен текст книги, разбитый по страницам. Система сохранения места последней прочитанной страницы, позволяет с удобством читать онлайн бесплатно книгу «Cyberphysical Smart Cities Infrastructures», без необходимости каждый раз заново искать на чём Вы остановились. Поставьте закладку, и сможете в любой момент перейти на страницу, на которой закончили чтение.

Тёмная тема
Сбросить

Интервал:

Закладка:

Сделать

2.4 Conclusion

We highlight smart cities' research branches and technology advancement regarding different complex domains. We propose a solution, namely, universal smart cities' decision making, which has three main sections: data capturing, data analysis, and decision making. We provide an abstract review of the fundamental concepts of big data, ML, and DL algorithms being applied to smart cities. We explore the essential role of the aforementioned algorithms on making decision within smart cities. The goal of this study is to provide a comprehensive survey of data analytics in smart cities, more specifically the role of big data algorithms and other advanced technologies like ML and DL for making decision in smart mobility within smart cities.

References

1 1 Amini, M.H., Mohammadi, J., and Kar, S. (2020). Promises of fully distributed optimization for IoT‐based smart city infrastructures. In: Optimization, Learning, and Control for Interdependent Complex Networks, ( M Hadi Amini), 15–35. Springer.

2 2 Montori, F., Bedogni, L., and Bononi, L. (2017). A collaborative internet of things architecture for smart cities and environmental monitoring. IEEE Internet of Things Journal 5 (2): 592–605.

3 3 Bibri, S.E. and Krogstie, J. (2017). Smart sustainable cities of the future: an extensive interdisciplinary literature review. Sustainable Cities and Society 31: 183–212.

4 4 Amini, M.H., Imteaj, A., and Pardalos, P.M. (2020). Interdependent networks: a data science perspective. Patterns 1 100003.

5 5 An, C. and Wu, C. (2020). Traffic big data assisted V2X communications toward smart transportation. Wireless Networks 26 (3): 1601–1610.

6 6 Iskandaryan, D., Ramos, F., and Trilles, S. (2020). Air quality prediction in smart cities using machine learning technologies based on sensor data: a review. Applied Sciences 10 (7): 2401.

7 7 Mohammadi, F.G., Amini, M.H., and Arabnia, H.R. (2020). An introduction to advanced machine learning: meta‐learning algorithms, applications, and promises. In: Optimization, Learning, and Control for Interdependent Complex Networks, ( M Hadi Amini), 129–144. Springer.

8 8 Neves, F.T., de Castro Neto M., and Aparicio, M. (2020). The impacts of open data initiatives on smart cities: a framework for evaluation and monitoring. Cities 106: 102860.

9 9 Amini, M.H., Arasteh, H., and Siano, P. (2019). Sustainable smart cities through the lens of complex interdependent infrastructures: panorama and state‐of‐the‐art. In: Sustainable Interdependent Networks II, ( M Hadi Amini), 45–68. Springer.

10 10 Park, J.H., Younas, M., Arabnia, H.R., and Chilamkurti, N. (2021). Emerging ICT applications and services—big data, IoT, and cloud computing. International journal of communication systems, 34 e4668.

11 11 Hossain, M.S., Muhammad, G., and Alamri, A. (2019). Smart healthcare monitoring: a voice pathology detection paradigm for smart cities. Multimedia Systems 25 (5): 565–575.

12 12 Rocha, N.P., Dias, A., Santinha, G. et al. (2019). Smart cities and healthcare: a systematic review. Technologies 7 (3): 58.

13 13 Ellaji, Ch., Sreehitha, G., and Devi, B.L. (2020). Efficient health care systems using intelligent things using NB‐IoT. Materials Today: Proceedings.

14 14 Hoang, G.T.T., Dupont, L., and Camargo, M. (2019). Application of decision‐making methods in smart city projects: a systematic literature review. Smart Cities 2 (3): 433–452.

15 15 de Oliveira, L.F.P., Manera, L.T., and Luz, P.D.G. (2020). Development of a smart traffic light control system with real‐time monitoring. IEEE Internet of Things Journal. 8 3384–3393.

16 16 Boulos, M.N.K., Peng, G., and VoPham, T. (2019). An overview of GeoAI applications in health and healthcare. International Journal of Health Geographics, 18 1–9.

17 17 Al‐Turjman, F., Nawaz, M.H., and Ulusar, U.D. (2020). Intelligence in the internet of medical things era: a systematic review of current and future trends. Computer Communications 150: 644–660.

18 18 Ullah, Z., Al‐Turjman, F., Mostarda, L., and Gagliardi, R. Applications of artificial intelligence and machine learning in smart cities. (2020). Computer Communications. 154 313–323.

19 19 Manikandan, R., Patan, R., Gandomi, A.H. et al. (2020). Hash polynomial two factor decision tree using IoT for smart health care scheduling. Expert Systems with Applications 141: 112924.

20 20 Khatri, C., Hedayatnia, B., Venkatesh, A. et al. (2018). Advancing the state of the art in open domain dialog systems through the Alexa prize. arXiv preprint arXiv:1812.10757.

21 21 Rak, M., Salzillo, G., and Romeo, C. (2020). Systematic IoT penetration testing: Alexa case study. ITASEC, pp. 190–200.

22 22 Elhoseny, H., Elhoseny, M., Riad, A.M., and Hassanien, A.E. (2018). A framework for big data analysis in smart cities. International Conference on Advanced Machine Learning Technologies and Applications, Springer, pp. 405–414.

23 23 Ju, J., Liu, L., and Feng, Y. (2018). Citizen‐centered big data analysis‐driven governance intelligence framework for smart cities. Telecommunications Policy 42 (10): 881–896.

24 24 Bhattacharya, S., Somayaji, S.R.K., Gadekallu, T.R. et al. (2020). A review on deep learning for future smart cities. Internet Technology Letters e187.

25 25 Kumar, S., Datta, D., Singh, S.K., and Sangaiah, A.K. (2018). An intelligent decision computing paradigm for crowd monitoring in the smart city. Journal of Parallel and Distributed Computing 118: 344–358.

26 26 Usman, M., Jan, M.A., He, X., and Chen, J. (2019). A survey on big multimedia data processing and management in smart cities. ACM Computing Surveys (CSUR) 52 (3): 1–29.

27 27 Mohammadi, F.G. and Abadeh, M.S. (2014). Image steganalysis using a bee colony based feature selection algorithm. Engineering Applications of Artificial Intelligence 31: 35–43.

28 28 Shenavarmasouleh, F. and Arabnia, H. (2019). Causes of misleading statistics and research results irreproducibility: a concise review. 2019 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 465–470.

29 29 Hashem, I.A.T., Chang, V., Anuar, N.B. et al. (2016). The role of big data in smart city. International Journal of Information Management 36 (5): 748–758.

30 30 Mohammadi, F.G., Shenavarmasouleh, F., Arabnia, H.R., and Amini, M.H. (2020). Impact of weather conditions on the Covid‐19 pandemic in the United States: a big data approach. 2020 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE.

31 31 Chakole, J.B., Kolhe, M.S., Mahapurush, G.D. et al. (2021). A Q‐learning agent for automated trading in equity stock markets. Expert Systems with Applications 163: 113761.

32 32 Liao, Z., Peng, J., Chen, Y. et al. (2020). A fast Q‐learning based data storage optimization for low latency in data center networks. IEEE Access 8: 90630–90639.

33 33 Fan, J., Wang, Z., Xie, Y., and Yang, Z. (2020). A theoretical analysis of deep Q‐learning. Learning for Dynamics and Control, PMLR, pp. 486–489.

34 34 Boussakssou, M., Hssina, B., and Erittali, M. (2020). Towards an adaptive E‐learning system based on Q‐learning algorithm. Procedia Computer Science 170: 1198–1203.

35 35 Joo, H., Ahmed, S.H., and Lim, Y. (2020). Traffic signal control for smart cities using reinforcement learning. Computer Communications 154: 324–330.

36 36 Wang, A., Zhang, A., Chan, E.H.W. et al. (2021). A review of human mobility research based on big data and its implication for smart city development. ISPRS International Journal of Geo‐Information 10 (1): 13.

37 37 Zhu, L., Yu, F.R., Wang, Y. et al. (2018). Big data analytics in intelligent transportation systems: a survey. IEEE Transactions on Intelligent Transportation Systems 20 (1): 383–398.

Читать дальше
Тёмная тема
Сбросить

Интервал:

Закладка:

Сделать

Похожие книги на «Cyberphysical Smart Cities Infrastructures»

Представляем Вашему вниманию похожие книги на «Cyberphysical Smart Cities Infrastructures» списком для выбора. Мы отобрали схожую по названию и смыслу литературу в надежде предоставить читателям больше вариантов отыскать новые, интересные, ещё непрочитанные произведения.


Отзывы о книге «Cyberphysical Smart Cities Infrastructures»

Обсуждение, отзывы о книге «Cyberphysical Smart Cities Infrastructures» и просто собственные мнения читателей. Оставьте ваши комментарии, напишите, что Вы думаете о произведении, его смысле или главных героях. Укажите что конкретно понравилось, а что нет, и почему Вы так считаете.

x