Scientific- Research Quarterly of Geographical Data (SEPEHR)

Scientific- Research Quarterly of Geographical Data (SEPEHR)

Analysis of the impact of emerging technologies on smart city development

Document Type : Research Paper

Authors
1 , Associate professor, Faculty of Entrepreneurship, University of Tehran, Tehran, Iran
2 Instructor, Malek Ashtar University of Technology, Tehran, Iran
3 Assistant professor , Faculty of family sciences, University of Tehran, Tehran, Iran
4 Ph.D. Student in entrepreneurship, Faculty of entrepreneurship, University of Tehran, Tehran, Iran
Abstract
Extended Abstract
Introduction
Smart cities have emerged as an innovative solution for optimal resource management and improving the quality of life. They leverage technologies such as artificial intelligence (AI), the Internet of Things (IoT), big data, and 5G networks to optimize urban services, enhance efficiency, and ensure more equitable resource distribution, thereby reducing spatial inequalities.
The main objective of this study was to identify and prioritize the most significant emerging technologies affecting smart city development and to analyze the level of expert consensus regarding their importance and adoption feasibility in urban management.
Materials and Methods
This research is applied in nature and follows a descriptive-analytical approach. A two-round fuzzy Delphi method was implemented: initially, questionnaires were distributed to 30 experts in urban technologies, and the collected data were analyzed. Subsequently, the coefficient of variation and fuzzy techniques were used to assess and consolidate the level of expert agreement.
Findings and Discussion
The findings indicate that AI and big data achieved the highest consensus and positive impact on improving urban decision-making, optimizing energy consumption, and enhancing service quality. Although IoT has high potential, it faces challenges due to the lack of unified technical standards and insufficient security infrastructure, which placed some IoT-related indicators at the threshold of acceptance. Meanwhile, 5G, as a key communication infrastructure, plays a crucial role in crisis management, intelligent transportation, and remote healthcare services.
Conclusion
These results highlight that realizing smart cities requires the standardization of IoT, the development of 5G infrastructure, and strategic investment in emerging technologies. Future research is recommended to adopt a systematic and comparative approach, focusing on technology localization, tailored policy frameworks, and exploring the social and cultural dimensions of technology implementation in diverse urban contexts.
Keywords
Subjects

1- Akbari, M., et al. (2020). 5G networks and their role in smart city development. Telecommunications Systems, 75(1), 123-134.
2- Alawadhi, S., et al. (2012). Building understanding of smart city initiatives. International Conference on Electronic Government, 40-53.
3- Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys & Tutorials, 17(4), 2347-2376.
4- Badura, M. (2017). Artificial intelligence in smart city applications. Smart Cities, 1(1), 47-58.
5- Ballas, A. (2013). What is a smart city?. Urban Studies, 50(11), 2207-2225.
6- Batty, M., Axhausen, K. W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., ... & Portugali, Y. (2012). Smart cities of the future. The European Physical Journal Special Topics, 214(1), 481518. https://doi.org/10.1140/epjst/e2012-01703-3
7- Biloslavo, R., & Dolinšek, S. (2010). Delphi technique as a tool for selecting and prioritizing measures for energy efficiency. Expert Systems with Applications, 37(12), 7915-7920.
8- Chen, C., et al. (2014). Big data and smart cities: Innovation and entrepreneurship opportunities. Journal of Urban Innovation, 2(1), 25-35.
9- Cheng, C. H., & Kuo, Y. F. (2008). An integrated fuzzy Delphi method and fuzzy analytic hierarchy process for multi-criteria decision making. Expert Systems with Applications, 34(1), 96-107.
10- Cheng, C. H., & Lin, C. C. (2002). Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation. European Journal of Operational Research, 142(1), 174-186.
11- Dirks, S., & Keeling, M. (2009). A vision of smarter cities: How cities can lead the way into a prosperous and sustainable future. IBM Global Business Services.
12- English, S. H., & Kernan, M. C. (1976). Assessment of agreement among judges. In E. S. Pearson & J. K. Hartley (Eds.), Biometrika Tables for Statisticians (Vol. 2, pp. 215-231). Cambridge University Press. (نقل شده در: Yang, J. B. (2003). Expert choice and the Delphi method: A fuzzy approach to decision making. European Journal of Operational Research, 151(3), 623-635.)
13- Ghaffarian Hoseini, A., et al. (2013). Smart buildings and sustainable development. Renewable and Sustainable Energy Reviews, 23, 169-177.
14- Giffinger, R., & Gudrun, H. (2010). Smart cities ranking: An effective instrument for the positioning of cities? ACE: Architecture, City and Environment, 4(12), 7-26.
15- Giffinger, R., Fertner, C., Kramar, H., Kalasek, R., Pichler-Milanovic, N., & Meijers, E. (2007). Smart cities: Ranking of European medium-sized cities. Centre of Regional Science, Vienna University of Technology.
16- Hancke, G. P., et al. (2013). The role of smart sensors in smart cities. IEEE Sensors Journal, 13(10), 3671-3680.
17- Harrison, C., & Donnelly, I. A. (2011). A theory of smart cities. Proceedings of the 55th Annual Meeting of the ISSS, 1-15.
18- Harrison, C., et al. (2010). Foundations for smarter cities. IBM Journal of Research and Development, 54(4), 1-16.
19- Hollands, R. G. (2008). Will the real smart city please stand up? City, 12(3), 303-320.
20- Hossain, M. S., et al. (2020). 5G-enabled smart healthcare systems. IEEE Network, 34(5), 140-147.
21- Kahraman, C., Cebeci, U., & Ulukan, Z. (2006). Multi-criteria supplier selection using fuzzy AHP. Logistics Information Management, 19(6), 382-394.
22- Kanter, J., & Litow, S. (2009). Smart cities: Turning data into innovation. IBM Institute for Business Value.
23- Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. SAGE Publications.
24- Klein, L., & Kaefer, F. (2008). Intelligent systems and smart cities. International Journal of Urban Technology, 15(2), 1-13.
25- Kloker, C., Mann, F., & Brunn, M. (2018). An iterative Delphi method for group decision-making under uncertainty. Decision Support Systems, 115, 31-41.
26- Komninos, N. (2002). Intelligent cities: Innovation, knowledge systems and digital spaces. Taylor & Francis.
27- Komninos, N. (2011). Intelligent cities: Variable geometries of spatial intelligence. Intelligent Buildings International, 3(3), 172-188.
28- Lazaroiu, G. C., & Roscia, M. (2012). Definition methodology for the smart cities model. Energy, 47(1), 326-332.
29- Li, S., et al. (2019). 5G for smart transportation systems: Overview and challenges. IEEE Communications Magazine, 57(1), 90-95.
30- Miorandi, D., Sicari, S., De Pellegrini, F., & Chlamtac, I. (2012). Internet of things: Vision, applications and research challenges. Ad Hoc Networks, 10(7), 1497-1516.
31- Murry, J. W., & Hammons, J. O. (1995). Delphi: A versatile methodology for conducting qualitative research. The Review of Higher Education, 18(4), 423-436.
32- Nam, T., & Pardo, T. A. (2011). Conceptualizing smart city with dimensions of technology, people, and institutions. Proceedings of the 12th Annual International Digital Government Research Conference, 282-291.
33- O’Grady, M., & O’Hare, G. M. P. (2012). Smart cities through networked intelligence. Proceedings of the 2012 IEEE International Conference on Systems, Man, and Cybernetics, 2308-2313.
34- Sadati Rezai Zadeh, H., et al. (1398). The impact of digital technologies on urban life. Journal of Urban Studies, 8(2), 125-140. [In Persian]
35- Saritas, O., & Aylen, J. (2010). Using futures methods to create innovation: Applying the Delphi method for nanotechnology foresight. Technological Forecasting and Social Change, 77(7), 1091-1101.
36- Sasanpour, M., & Hatami, M. (1396). The role of ICT in modern society. Information Technology Journal, 14(3), 45-60. [In Persian]
37- Shi, Y., et al. (2020). Artificial intelligence applications in smart cities. Sensors, 20(4), 1231.
38- Singh, K., Singh, S., & Łasak, K. (2020). Blockchain and AI for smart city solutions: Challenges and opportunities. Sustainable Cities and Society, 60, 102234.
39- Su, C. T., Chou, S. W., & Wang, K. S. (2010). A fuzzy Delphi method for improving quality of service in Internet banking. Expert Systems with Applications, 37(12), 8123-8130.
40- Ullah, I., et al. (2020). AI in smart healthcare: Applications and challenges. Healthcare, 8(2), 102.
41- Urashima, K., Fukushige, S., & Onaga, K. (2012). A Delphi study for identifying the needs of future social systems in Japan. Foresight, 14(4), 271-283.
42- Washburn, D., Sindhu, U., Balaouras, S., Dines, R. A., Hayes, N. M., & Nelson, L. E. (2010). Helping CIOs understand “smart city” initiatives. Forrester Research Report.
43- Yigitcanlar, T., Kamruzzaman, M., & Foth, M. (2018). Big data analytics for smart cities. Journal of Urban Technology, 25(1), 3-19.
44- Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of things for smart cities. IEEE Internet of Things Journal, 1(1), 22-32.
45- Zhang, Y., et al. (2020). Big data analytics for urban sustainability. Sustainable Cities and Society, 61, 102322.
46- Zhou, Z., et al. (2020). 5G-enabled IoT for smart cities. IEEE Network, 34(5), 122-128.
47- Zygiaris, S. (2013). Smart city reference model: Assisting planners to conceptualize the building of smart city innovation ecosystems. Journal of the Knowledge Economy, 4(2), 217-231.