AI-enhanced IoT architectures for intelligent and sustainable smart city networks

Authors

https://doi.org/10.22105/sci.v2i1.32

Abstract

The emergence of smart cities has necessitated advanced, efficient, and scalable networking solutions capable of managing vast amounts of data generated by the Internet of Things (IoT). Leveraging Artificial Intelligence (AI) alongside IoT infrastructure offers transformative potential in optimizing smart city operations. This paper explores the integration of AI with IoT to enhance networking capabilities for smart cities. It examines AI-driven optimization techniques, potential applications across different urban systems, and the challenges in implementing these technologies. It offers insights into how AI-enhanced IoT networks can support sustainable, resilient, and citizen-centered urban environments.

Keywords:

Internet of things, Artificial intelligence, AI-driven optimization

References

  1. [1] 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. https://doi.org/10.1109/JIOT.2014.2306328
  2. [2] Khan, L. U., Yaqoob, I., Imran, M., Han, Z., & Hong, C. S. (2020). 6G wireless systems: A vision, architectural elements, and future directions. IEEE access, 8, 147029–147044. https://doi.org/10.1109/ACCESS.2020.3015289
  3. [3] 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), 481–518. https://doi.org/10.1140/epjst/e2012-01703-3
  4. [4] Talari, S., Shafie-Khah, M., Siano, P., Loia, V., Tommasetti, A., & Catalão, J. P. S. (2017). A review of smart cities based on the internet of things concept. Energies, 10(4), 421. https://doi.org/10.3390/en10040421
  5. [5] Xu, L. Da, He, W., & Li, S. (2014). Internet of things in industries: A survey. IEEE transactions on industrial informatics, 10(4), 2233–2243. https://doi.org/10.1109/TII.2014.2300753
  6. [6] Alsheikh, M. A., Lin, S., Niyato, D., & Tan, H. P. (2014). Machine learning in wireless sensor networks: Algorithms, strategies, and applications. IEEE communications surveys and tutorials, 16(4), 1996–2018. https://doi.org/10.1109/COMST.2014.2320099
  7. [7] Dastjerdi, A. V., & Buyya, R. (2016). Fog computing: Helping the internet of things realize its potential. Computer, 49(8), 112–116. https://doi.org/10.1109/MC.2016.245
  8. [8] Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE internet of things journal, 3(5), 637–646. https://doi.org/10.1109/JIOT.2016.2579198
  9. [9] Meireles, T., Fonseca, J., & Ferreira, J. (2016). Deterministic vehicular communications supported by the roadside infrastructure: A case study. In Intelligent transportation systems: dependable vehicular communications for improved road safety (pp. 49–80). Springer. https://doi.org/10.1007/978-3-319-28183-4_3
  10. [10] Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012). Fog computing and its role in the internet of things [presentation]. Proceedings of the first edition of the mcc workshop on mobile cloud computing (pp. 13–16). https://doi.org/10.1145/2342509.234251
  11. [11] Alahakoon, D., & Yu, X. (2015). Smart electricity meter data intelligence for future energy systems: A survey. IEEE transactions on industrial informatics, 12(1), 425–436. https://doi.org/10.1109/TII.2015.2414355
  12. [12] Vijayan, V., Mohapatra, A., & Singh, S. N. (2021). Demand response with volt/var optimization for unbalanced active distribution systems. Applied energy, 300, 117361. https://doi.org/10.1016/j.apenergy.2021.117361
  13. [13] Kerper, M., Wewetzer, C., Sasse, A., & Mauve, M. (2012, May). Learning traffic light phase schedules from velocity profiles in the cloud. 2012 5th international conference on new technologies, mobility and security (NTMS) (pp. 1-5). IEEE. https://doi.org/10.1109/NTMS.2012.6208704
  14. [14] D’Amico, G., Szopik-Depczyńska, K., Dembińska, I., & Ioppolo, G. (2021). Smart and sustainable logistics of Port cities: A framework for comprehending enabling factors, domains and goals. Sustainable cities and society, 69, 102801. https://doi.org/10.1016/j.scs.2021.102801
  15. [15] Sicari, S., Rizzardi, A., Grieco, L. A., & Coen-Porisini, A. (2015). Security, privacy and trust in Internet of Things: The road ahead. Computer networks, 76, 146–164. https://doi.org/10.1016/j.comnet.2014.11.008
  16. [16] Roman, R., Zhou, J., & Lopez, J. (2013). On the features and challenges of security and privacy in distributed internet of things. Computer networks, 57(10), 2266–2279. https://doi.org/10.1016/j.comnet.2012.12.018
  17. [17] Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of things (IoT): A vision, architectural elements, and future directions. Future generation computer systems, 29(7), 1645–1660. https://doi.org/10.1016/j.future.2013.01.010
  18. [18] Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM transactions on intelligent systems and technology, 10(2), 1–19. https://doi.org/10.1145/3298981
  19. [19] Gyongyosi, L., & Imre, S. (2019). A survey on quantum computing technology. Computer science review, 31, 51–71. https://doi.org/10.1016/j.cosrev.2018.11.002
  20. [20] Chiang, M., & Zhang, T. (2016). Fog and IoT: An overview of research opportunities. IEEE internet of things journal, 3(6), 854–864. https://doi.org/10.1109/JIOT.2016.2584538

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Published

2025-03-26

Issue

Vol. 2 No. 1 (2025): Smart City Insights

Section

Articles

Categories

How to Cite

Karimi, H., Nozick, V., & Rasoulpour, F. (2025). AI-enhanced IoT architectures for intelligent and sustainable smart city networks. Smart City Insights, 2(1), 41-46. https://doi.org/10.22105/sci.v2i1.32

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