«SMART CITY» IN THE CONTEXT OF INTELEGENT SYSTEM AND BIG DATA: STRATEGIES, RISKS

Array

Authors

  • V. Boyko National University "Odessa Law Academy"
  • M. Vasilenko National University "Odessa Law Academy"

Keywords:

smart city, information ecosystems, cybersecurity, municipal economy, risks, threats, Big Data, Artificial Intelligence.

Abstract

According to UN forecasts, by 2050 more than two-thirds of the world’s population will live in cities. Urban and rural areas are evolving and their evolution are based on wide use of broadband Internet systems, cloud computing platforms, geoinformation and geo-positioning systems, high-load computing clusters, wireless telecommunications, “Internet of Things” systems and other technological and information innovations. With the increasing complexity and cohesion of urban systems, the cost of management decisions - and the associated cost of decision errors - has increased significantly. The time for deciding has on the contrary decreased. Incoming data may be deliberately inaccurate, unreliable, clogged with random and intentional interference. And in many cases, it is the management decision that is a critical factor for development and proper functioning of the urban system especially in the context of the formation of a smart city infrastructure. The paper studies use cases of artificial intelligence systems (AI) for processing big data and decision support as a solution to the problems listed above. Use of AI systems allow collecting and cleaning data to obtain a reliable information landscape of the urban systems. Further, on the basis of the obtained picture, AI systems can be used for operational analysis and response to emerging crisis situations, for analyzing the medium-term perspective and balancing the optimal use of urban resources, for long-term planning of the urban environment development. Currently, according to experts, there are two main strategies for the development of information systems - vertical and horizontal. The article analyzes the possibility of applying these two strategies to the use of AI in an urban environment. Using the example of the implementation experience (ET City Brain), on the one hand, conclusions can be drawn about the long-term benefits of such an implementation, on the other, about the risks associated with "vendor lock-in" and the associated problems. One of the biggest risks is the subsequent monopolization of the management system, which transfers part of the power from city structures to the owners of the information system, who, in such conditions, gain the right to vote and leverage on municipalities. It is shown that maximal use of open data and open source software solutions are the most beneficial from the point of view from the point of view of the city and urban systems as stakeholders in the formation of a smart city.

Author Biographies

V. Boyko, National University "Odessa Law Academy"

PhD, Associate Professor of the Department

M. Vasilenko, National University "Odessa Law Academy"

Doctor of Sciences, Professor, Head of the Department

References

1. Ritchie H., Roser M. (2018). Urbanization. Our world in data. URL: https://ourworldindata.org/urbanization#:~:text=In%202007%2C%20urban%20and%20rural,equal%20at%203.33%20billion%20each.&text=UN%20estimates%20therefore%20report%20that,55%20percent%20of%20the%20world
2. Dauvergne P. (2021). The globalization of artificial intelligence: consequences for the politics of environmentalism //Globalizations. Is. 18. Vol. 2. 285–299.
3. Angelidou M. Smart cities: A conjuncture of four forces // Cities. — 2015. — Т. 47. — С. 95–106.
4. Bibri S. E., Krogstie J. (2017). ICT of the new wave of computing for sustainable urban forms: Their big data and context-aware augmented typologies and design concepts // Sustainable Cities and Society. Vol. 32. 449–474.
5. Lim C., Kim K.-J., Maglio P. P. (2018). Smart cities with big data: Reference models, challenges, and considerations // Cities. — Elsevier, Vol. 82. 86–99.
6. Boyko V., Vasilenko M. (2020). «Smart city» in the context of cybersecurity: Incidents, risks, threats // Municipal economy of cities. Vol. 4, no. 157. 184–191.
7. Boyko V., Vasilenko M. (2020). Cybersecurity of smart cities: Social aspects, risks of deanonymization and doxing // Municipal economy of cities. Vol. 6, no. 159. 186–195.
8. O’Leary D. E. (2013). Artificial intelligence and big data // IEEE Intelligent Systems. Vol. 28, no. 2. 96–99.
9. Saha B., Srivastava D. (2014). Data quality: The other face of big data / 2014 ieee 30th international conference on data engineering. 1294–1297.
10. Bhattacharya S., Somayaji S. R. K., Gadekallu T. R., Alazab M., Maddikunta P. K. R. A review on deep learning for future smart cities // Internet Technology Letters. Vol. n/a, no. n/a. e187.
11. Caprotti F., Liu D. (2020). Platform urbanism and the Chinese smart city: The co-production and territorialisation of hangzhou city brain. GeoJournal. — Springer, 1–15.
12. Thomas E., Geoffrey P., Marshall V. A. (2011). Platform envelopment. Strategic Management Journal. — John Wiley; Sons, Vol. 32, no. 12. 1270–1285.
13. Kenney M., Zysman J. (2016). The rise of the platform economy. Issues in science and technology. — Issues in Science; Technology, Vol. 32, no. 3. 61.
14. Jia K., Kenney M. (2016). Mobile internet business models in china: Vertical hierarchies, horizontal conglomerates, or business groups. Berkeley Roundtable on the International Economy Working Paper. Vol. 6. Retrieved from: https://brie.berkeley.edu/sites/default/files/working-paper-2016-6.jiakenney.pdf
15. Jia K., Kenney M., Mattila J., Seppala T. (2018). The application of artificial intelligence at Chinese digital platform giants: Baidu, alibaba and tencent. ETLA reports. Is. 81. Retrieved from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3154038
16. Jackson P. (1998). Introduction to expert systems. — Addison-Wesley Longman Publishing Co., Inc., 542.
17. Wu X., Kumar V., Quinlan J. R., Ghosh J., Yang Q., Motoda H., McLachlan G. J., Ng A., Liu B., Philip S. Y., others. (2008). Top 10 algorithms in data mining. Knowledge and information systems. — Springer, Vol. 14, no. 1. 1–37.
18. Settouti N., Bechar M. E. A., Chikh M. A. (2016). Statistical comparisons of the top 10 algorithms in data mining for classification task // International Journal of Interactive Multimedia and Artificial Intelligence. Vol. 4, no. 1. 46–51.
19. Bojko V. (2017). Sobytijno-orientirovannoe modelirovanie vosstanovleniya rabotosposobnosti slozhnykh organizacionno-tekhnicheskikh sistem // Rozvytok transportu. — Odeskyi natsionalnyi morskyi universytet, Is. 1. 161–170
20. Appio F. P., Lima M., Paroutis S. (2019). Understanding smart cities: Innovation ecosystems, technological advancements, and societal challenges // Technological Forecasting and Social Change. — Elsevier, Vol. 142. 1–14.
21. Kong L., Liu Z., Wu J. (2020). A systematic review of big data-based urban sustainability research: State-of-the-science
and future directions // Journal of Cleaner Production. — Elsevier, 123–142.
22. The Chronicles of Cloud Building in Hangzhou: Part 1 (2020). Alibaba Cloud Community. Retrieved from: https://www.alibabacloud.com/blog/the-chronicles-of-cloud-building-in-hangzhou-part-1_594241
23. The Chronicles of Cloud Building in Hangzhou: Part 2 (2020). Alibaba Cloud Community. Retrieved from: https://www.alibabacloud.com/blog/the-chronicles-of-cloud-building-in-hangzhou-part-2_594242
24. City Brain Now in 23 Cities in Asia (2020). Alibaba Cloud Community. Retrieved from: https://www.alibabacloud.com/blog/city-brain-now-in-23-cities-in-asia_595479
25. Raymond E. (1999). The cathedral and the bazaar // Knowledge, Technology & Policy. — Springer 9. Vol. 12, no. 3. 23–49.
26. Raymond E. S., others. (1999). The magic cauldron. — Citeseer, Retrvied from: http://www.catb.org/~esr/writings/magic-cauldron/magic-cauldron.html
27. Weber S. (2009). The success of open source. — Harvard University Press. Retrieved from: https://www.hup.harvard.edu/catalog.php?isbn=9780674018587

Published

2021-03-26

How to Cite

Boyko, V., & Vasilenko, M. (2021). «SMART CITY» IN THE CONTEXT OF INTELEGENT SYSTEM AND BIG DATA: STRATEGIES, RISKS: Array. Municipal Economy of Cities, 1(161), 241–249. Retrieved from https://khg.kname.edu.ua/index.php/khg/article/view/5741