Securing the Future: AI-Driven Data Transmission in IoT-Powered Smart Cities

Authors

  • Aurgho Banerjee * * School of Computer Science Engineering, KIIT University, Bhubaneswar, India.

https://doi.org/10.22105/scfa.v2i1.42

Abstract

The swift adoption of Artificial Intelligence (AI) alongside the Internet of Things (IoT) has revolutionized smart cities, allowing for improved urban services such as traffic control, energy management, healthcare delivery, and public safety. However, the extensive generation and transmission of sensitive data by IoT devices present substantial cybersecurity risks. Unprotected data transmission can result in privacy violations, unauthorized access, and vulnerabilities within systems, jeopardizing the integrity and efficiency of smart city frameworks. This paper investigates the essential elements of secure data transmission within AI-driven IoT networks. It analyzes different encryption techniques, AI-enhanced Intrusion Detection Systems (IDSs), and decentralized frameworks based on blockchain to guarantee data integrity and confidentiality. Moreover, we emphasize the importance of federated learning, which enables distributed AI models to enhance their performance while keeping sensitive information localized, thereby reducing the likelihood of data breaches. Significant challenges are addressed, including the computational constraints of IoT devices, the diversity of IoT networks, and the requirement for low-latency communication in real-time scenarios. Innovative solutions, such as quantum-resistant cryptography and the potential of 6G technology, are also examined. The paper concludes by outlining future research and development pathways aimed at improving the security, scalability, and efficiency of IoT networks within smart cities.

Keywords:

Smart cities, Artificial intelligence-enabled internet of things, Secure data transmission, Blockchain, Federated learning

References

  1. [1] [1] Van Hoang, T. (2024). Impact of integrated artificial intelligence and internet of things technologies on smart city transformation. Journal of technical education science, 19(1 (Spec. Issue)), 64–73. https://doi.org/10.54644/jte.2024.1532
  2. [2] [2] Yao, Y. (2022). A review of the comprehensive application of big data, artificial intelligence, and internet of things technologies in smart cities. Journal of computational methods in engineering applications, 2(1), 1–10. https://doi.org/10.62836/jcmea.v2i1.0004
  3. [3] [3] Aminizadeh, S., Heidari, A., Dehghan, M., Toumaj, S., Rezaei, M., Jafari Navimipour, N., … Unal, M. (2024). Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service. Artificial intelligence in medicine, 149, 102779. https://doi.org/10.1016/j.artmed.2024.102779
  4. [4] [4] Shahbazi, Z., Shahbazi, Z., & Nowaczyk, S. (2024). Enhancing air quality forecasting using machine learning techniques. IEEE access, 12, 197290–197299. https://doi.org/10.1109/ACCESS.2024.3516883
  5. [5] [5] Maple, C. (2017). Security and privacy in the internet of things. Journal of cyber policy, 2(2), 155–184. https://doi.org/10.1080/23738871.2017.1366536
  6. [6] [6] Yang, Y., Wu, L., Yin, G., Li, L., & Zhao, H. (2017). A survey on security and privacy issues in internet-of-things. IEEE internet of things journal, 4(5), 1250–1258. https://doi.org/10.1109/JIOT.2017.2694844
  7. [7] [7] Escamilla-Ambrosio, P. J., Rodríguez-Mota, A., Aguirre-Anaya, E., Acosta-Bermejo, R., & Salinas-Rosales, M. (2018). Distributing computing in the internet of things: cloud, fog and edge computing overview. In NEO 2016 (pp. 87–115). Cham: Springer, Cham. https://doi.org/10.1007/978-3-319-64063-1_4
  8. [8] [8] Yu, W., Liang, F., He, X., Hatcher, W. G., Lu, C., Lin, J., & Yang, X. (2018). A survey on the edge computing for the internet of things. IEEE access, 6, 6900–6919. https://doi.org/10.1109/ACCESS.2017.2778504
  9. [9] [9] Zeadally, S., Isaac, J. T., & Baig, Z. (2016). Security attacks and solutions in electronic health (E-health) systems. Journal of medical systems, 40(12), 263. https://doi.org/10.1007/s10916-016-0597-z
  10. [10] [10] Thakor, V. A., Razzaque, M. A., & Khandaker, M. R. A. (2021). Lightweight cryptography algorithms for resource-constrained IoT devices: A review, comparison and research opportunities. IEEE access, 9, 28177–28193. https://doi.org/10.1109/ACCESS.2021.3052867
  11. [11] [11] Lv, Z., Qiao, L., Kumar Singh, A., & Wang, Q. (2021). AI-empowered IoT Security for smart cities. ACM transactions internet technology, 21(4), 1–21. https://doi.org/10.1145/3406115
  12. [12] [12] Ajala, O. A., & Balogun, O. A. (2024). Leveraging AI/ML for anomaly detection, threat prediction, and automated response. World journal of advanced research and reviews, 21(1), 2584–2598. https://doi.org/10.30574/wjarr.2024.21.1.0287
  13. [13] [13] Liao, H. J., Richard Lin, C. H., Lin, Y. C., & Tung, K. Y. (2013). Intrusion detection system: A comprehensive review. Journal of network and computer applications, 36(1), 16–24. https://doi.org/10.1016/j.jnca.2012.09.004
  14. [14] [14] Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Vincent Poor, H. (2021). Federated learning for internet of things: A comprehensive survey. IEEE communications surveys & tutorials, 23(3), 1622–1658. https://doi.org/10.1109/COMST.2021.3075439
  15. [15] [15] Alahi, M. E. E., Sukkuea, A., Tina, F. W., Nag, A., Kurdthongmee, W., Suwannarat, K., & Mukhopadhyay, S. C. (2023). Integration of IoT-enabled technologies and artificial intelligence (AI) for smart city scenario: recent advancements and future trends. Sensors, 23(11), 5206. https://doi.org/10.3390/s23115206
  16. [16] [16] Mamun, M. A. Al, & Yuce, M. R. (2019). Sensors and systems for wearable environmental monitoring toward IoT-enabled applications: A review. IEEE sensors journal, 19(18), 7771–7788. https://doi.org/10.1109/JSEN.2019.2919352
  17. [17] [17] Cominola, A., Giuliani, M., Piga, D., Castelletti, A., & Rizzoli, A. E. (2015). Benefits and challenges of using smart meters for advancing residential water demand modeling and management: A review. Environmental modelling & software, 72, 198–214. https://doi.org/10.1016/j.envsoft.2015.07.012
  18. [18] [18] Jain, N. K., Saini, R. K., & Mittal, P. (2019). A review on traffic monitoring system techniques. Soft computing: theories and applications (pp. 569–577). Singapore: Springer, Singapore. https://doi.org/10.1007/978-981-13-0589-4_53
  19. [19] [19] Dias, D., & Paulo Silva Cunha, J. (2018). Wearable health devices—vital sign monitoring, systems and technologies. Sensors, 18(8), 2424. https://doi.org/10.3390/s18082414
  20. [20] [20] Thamarai, M., & Naresh, V. S. (2023). Smart self-power generating garbage management system using deep learning for smart cities. Microprocessors and microsystems, 98, 104816. https://doi.org/10.1016/j.micpro.2023.104816
  21. [21] [21] El Khaled, Z., Mcheick, H., & Petrillo, F. (2019). WiFi coverage range characterization for smart space applications. 2019 IEEE/ACM 1st international workshop on software engineering research & practices for the internet of things (SERP4IoT) (pp. 61–68). IEEE. https://doi.org/10.1109/SERP4IoT.2019.00018
  22. [22] [22] Adewale, T., & Paul, J. (2024). AI, 5G, and IoT: how these technologies are creating the perfect storm for smart systems. https://www.researchgate.net/publication/385855348
  23. [23] [23] de Carvalho Silva, J., Rodrigues, J. J. P. C., Alberti, A. M., Solic, P., & Aquino, A. L. L. (2017). LoRaWAN — a low power wan protocol for internet of things: a review and opportunities. 2017 2nd international multidisciplinary conference on computer and energy science (SpliTech) (pp. 1–6). IEEE. https://ieeexplore.ieee.org/abstract/document/8019271/authors#authors
  24. [24] [24] Zhang, T., Lu, J., Hu, F., & Hao, Q. (2014). Bluetooth low energy for wearable sensor-based healthcare systems. 2014 IEEE healthcare innovation conference (HIC) (pp. 251–254). IEEE. https://doi.org/10.1109/HIC.2014.7038922
  25. [25] [25] Lee, J. S., Chuang, C. C., & Shen, C. C. (2009). Applications of short-range wireless technologies to industrial automation: A zigbee approach. 2009 fifth advanced international conference on telecommunications (pp. 15–20). IEEE. https://doi.org/10.1109/AICT.2009.9
  26. [26] [26] Majumdar, S., Subhani, M. M., Roullier, B., Anjum, A., & Zhu, R. (2021). Congestion prediction for smart sustainable cities using IoT and machine learning approaches. Sustainable cities and society, 64, 102500. https://doi.org/10.1016/j.scs.2020.102500
  27. [27] [27] Salama, R., Mohapatra, H., Tülbentçi, T., & Al-Turjman, F. (2025). Deep learning technology: enabling safe communication via the internet of things. Frontiers in communications and networks, 6, 1416845. https://doi.org/10.3389/frcmn.2025.1416845
  28. [28] [28] Shafik, W. (2024). Deep learning impacts in the field of artificial intelligence. In deep learning concepts in operations research (pp. 9–26). Auerbach Publications. https://doi.org/10.1201/9781003433309
  29. [29] [29] Maadi, S., Stein, S., Hong, J., & Murray-Smith, R. (2022). Real-time adaptive traffic signal control in a connected and automated vehicle environment: optimisation of signal planning with reinforcement learning under vehicle speed guidance. Sensors, 22(19), 7501. https://doi.org/10.3390/s22197501
  30. [30] [30] Panda, A. K., Lenka, A. A., Mohapatra, A., Rath, B. K., Parida, A. A., & Mohapatra, H. (2025). Integrating cloud computing for intelligent transportation solutions in smart cities: A short review. In interdisciplinary approaches to transportation and urban planning (pp. 121–142). IGI Global. http://doi.org/10.4018/979-8-3693-6695-0.ch005
  31. [31] [31] Rathee, G., Khelifi, A., & Iqbal, R. (2021). Artificial intelligence-(AI-) enabled internet of things (IoT) for secure big data processing in multihoming networks. Wireless communications and mobile computing, 2021(1), 5754322. https://doi.org/10.1155/2021/5754322
  32. [32] [32] Pereira, F., Correia, R., Pinho, P., Lopes, S. I., & Carvalho, N. B. (2020). Challenges in resource-constrained IoT devices: energy and communication as critical success factors for future IoT deployment. Sensors, 20(22), 6420. https://doi.org/10.3390/s20226420
  33. [33] [33] Fera, M. A., & Priya, M. S. (2016). A survey on trusted platform module for data remanence in cloud. Proceedings of the international conference on soft computing systems (pp. 689–695). Springer, New Delhi. https://doi.org/10.1007/978-81-322-2674-1_65
  34. [34] [34] Siemens. (2020). Totally integrated automation — future inside. https://new.siemens.com/global/en/products/automation/topic-areas/tia.html

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Published

2025-03-21

Issue

Vol. 2 No. 1 (2025): Soft Computing Fusion with Applications

Section

Articles

How to Cite

Securing the Future: AI-Driven Data Transmission in IoT-Powered Smart Cities. (2025). Soft Computing Fusion With Applications , 2(1), 33-53. https://doi.org/10.22105/scfa.v2i1.42

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