Cloud and edge computing collaboration for IoT-enabled traffic monitoring

Authors

  • Sohom Chakraborty * School of Computer Engineering, KIIT (Deemed to Be) University, Bhubaneswar -751024, Odisha, India

https://doi.org/10.22105/metaverse.v2i1.53

Abstract

This paper explores a collaborative cloud and edge computing framework to enhance Internet of Things (IoT)-based traffic monitoring systems, vital for managing congestion in growing urban areas. With increasing vehicle numbers and urban density, traffic congestion is a significant challenge, leading to delays, pollution, and lower quality of life. IoT-enabled devices, like sensors and cameras, provide real-time monitoring for improved traffic management. However, the large data volume demands a hybrid infrastructure for efficient, secure handling. Our framework leverages cloud and edge computing to address these requirements. Processing data locally at the edge reduces latency, optimizes bandwidth, and boosts data security—essential for IoT in busy urban environments. Meanwhile, the cloud component enables advanced analytics and storage, supporting historical data analysis, predictive modeling, and long-term storage. Our study shows that this collaborative approach lowers latency and data transfer costs and improves the scalability and efficiency of smart traffic management. This research demonstrates the potential of a cloud-edge hybrid framework to transform traffic systems, offering a more adaptable, responsive, and sustainable solution for modern cities.

Keywords:

Cloud computing, Edge computing, Internet of things, Traffic monitoring, Smart cities

References

  1. [1] Musa, A. A., Malami, S. I., Alanazi, F., Ounaies, W., Alshammari, M., & Haruna, S. I. (2023). Sustainable traffic management for smart cities using internet-of-things-oriented intelligent transportation systems (ITS): Challenges and recommendations. Sustainability, 15(13), 9859. https://doi.org/10.3390/su15139859
  2. [2] Hoang, T. Van. (2024). Impact of integrated artificial intelligence and internet of things technologies on smart city transformation. Journal of technical education science, 19(1), 64–73. https://doi.org/10.54644/jte.2024.1532
  3. [3] Shafik, W., Matinkhah, S. M., & Ghasemzadeh, M. (2020). Internet of things-based energy management, challenges, and solutions in smart cities. Journal of communications technology, electronics and computer science, 27, 1–11. https://ojs.jctecs.com/index.php/com/article/view/302
  4. [4] Ficili, I., Giacobbe, M., Tricomi, G., & Puliafito, A. (2025). From sensors to data intelligence: Leveraging IoT, cloud, and edge computing with AI. Sensors, 25(6), 1763. https://doi.org/10.3390/s25061763
  5. [5] Pourqasem, J. (2024). Transforming user experience in the metaverse through edge technology. Metaversalize, 1(1), 21–31. https://doi.org/10.22105/metaverse.v1i1.19
  6. [6] He, Q., Xi, Z., Feng, Z., Teng, Y., Ma, L., Cai, Y., & Yu, K. (2024). Telemedicine monitoring system based on fog/edge computing: A survey. IEEE transactions on services computing. https://doi.org/10.1109/TSC.2024.3506473
  7. [7] McEnroe, P., Wang, S., & Liyanage, M. (2022). A survey on the convergence of edge computing and AI for UAVs: Opportunities and challenges. IEEE internet of things journal, 9(17), 15435–15459. https://doi.org/10.1109/JIOT.2022.3176400
  8. [8] Oladimeji, D., Gupta, K., Kose, N. A., Gundogan, K., Ge, L., & Liang, F. (2023). Smart transportation: An overview of technologies and applications. Sensors, 23(8), 3880. https://doi.org/10.3390/s23083880
  9. [9] Gohar, A., & Nencioni, G. (2021). The role of 5g technologies in a smart city: The case for intelligent transportation system. Sustainability, 13(9), 5188. https://doi.org/10.3390/su13095188
  10. [10] Yang, C., Lan, S., Wang, L., Shen, W., & Huang, G. G. Q. (2020). Big data driven edge-cloud collaboration architecture for cloud manufacturing: A software defined perspective. IEEE access, 8, 45938–45950. https://doi.org/10.1109/ACCESS.2020.2977846
  11. [11] Zhou, Z., Yu, S., Chen, W., & Chen, X. (2020). CE-IoT: Cost-effective cloud-edge resource provisioning for heterogeneous IoT applications. IEEE internet of things journal, 7(9), 8600–8614. https://doi.org/10.1109/JIOT.2020.2994308
  12. [12] 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
  13. [13] Feng, C., Han, P., Zhang, X., Yang, B., Liu, Y., & Guo, L. (2022). Computation offloading in mobile edge computing networks: A survey. Journal of network and computer applications, 202, 103366. https://doi.org/10.1016/j.jnca.2022.103366
  14. [14] Gobinath, S., Karuppannan, A., Vijikala, V., Radhika, K., & Gowrishankar, J. (2025). Integration of cloud and edge computing in distributed renewable energy systems. In Digital innovations for renewable energy and conservation (pp. 195–218). IGI Global. https://doi.org/10.4018/979-8-3693-6532-8.ch009
  15. [15] Gong, T., Zhu, L., Yu, F. R., & Tang, T. (2023). Edge intelligence in intelligent transportation systems: A survey. IEEE transactions on intelligent transportation systems, 24(9), 8919–8944. https://doi.org/10.1109/TITS.2023.3275741
  16. [16] Mukherjee, M., Matam, R., Mavromoustakis, C. X., Jiang, H., Mastorakis, G., & Guo, M. (2020). Intelligent edge computing: security and privacy challenges. IEEE communications magazine, 58(9), 26–31. https://doi.org/10.1109/MCOM.001.2000297
  17. [17] Liang, Q., Hanafy, W. A., Ali-Eldin, A., & Shenoy, P. (2023). Model-driven cluster resource management for AI workloads in edge clouds. ACM transactions on autonomous and adaptive systems, 18(1), 1–26. https://doi.org/10.1145/3582080

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Published

2025-03-10

Issue

Vol. 2 No. 1 (2025): Metaversalize

Section

Articles

Categories

How to Cite

Chakraborty, S. . (2025). Cloud and edge computing collaboration for IoT-enabled traffic monitoring. Metaversalize, 2(1), 51-59. https://doi.org/10.22105/metaverse.v2i1.53
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