A Comprehensive Review on Machine Learning enabled IoT and 6G Framework for Optimal Shortest Path Computation in Smart City Logistics
Abstract
As urban transportation systems are becoming more complex, smart city logistics require efficient and adaptive routing mechanisms to address them. The dynamic traffic conditions and large scale network environments make traditional shortest path algorithms struggle to operate effectively. This review studies the recent upgrades in machine learning (ML) based routing, internet of things (IoT) enabled data collection and emerging 6G communication techniques for intelligent shortest path predictions. ML approaches such as graph neural networks and reinforcement learning use historic and real time data to predict and adapt the routing process. On the other side, IoT collects data from various sources such as connected vehicles, sensors and urban settings to improve situational awareness. Edge computing also supports the low-latency decision-making while 6G networks give mass connectivity, reliable communication, and integrated intelligence for live coordination across distributed networks. Advanced architectures like federated learning, digital twins, and multi-agent systems are examined in the paper to observe how they improve scalability, privacy and system efficiency. However, many challenges are also discussed such as data heterogeneity, scalability, real time restrictions, security risks and lack of explainability and reliability along with the future scope. These developments highlight the potential of unified ML-IoT-6G structure to allow scalable, efficient and intelligent transportation systems for smart cities.References
[1] Pan, S., Zhou, W., Piramuthu, S., Giannikas, V. and Chen, C., 2021. Smart city for sustainable urban freight logistics. International Journal of Production Research, 59(7), pp.2079-2089.
[2] Chung, S.H., 2021. Applications of smart technologies in logistics and transport: A review. Transportation Research Part E: Logistics and Transportation Review, 153, p.102455.
[3] Savelsbergh, M. and Van Woensel, T., 2016. 50th anniversary invited article—city logistics: Challenges and opportunities. Transportation science, 50(2), pp.579-590.
[4] Delling, D., Sanders, P., Schultes, D. and Wagner, D., 2009. Engineering route planning algorithms. In Algorithmics of large and complex networks: design, analysis, and simulation (pp. 117-139). Berlin, Heidelberg: Springer Berlin Heidelberg.
[5] Nosratabadi, S., Mosavi, A., Keivani, R., Ardabili, S. and Aram, F., 2019, September. State of the art survey of deep learning and machine learning models for smart cities and urban sustainability. In International conference on global research and education (pp. 228-238). Cham: Springer International Publishing.
[6] Zhang, S., Luo, Z., Yang, L., Teng, F. and Li, T., 2024. A survey of route recommendations: Methods, applications, and opportunities. Information Fusion, 108, p.102413.
[7] Ullah, A., Anwar, S.M., Li, J., Nadeem, L., Mahmood, T., Rehman, A. and Saba, T., 2024. Smart cities: The role of Internet of Things and machine learning in realizing a data-centric smart environment. Complex & Intelligent Systems, 10(1), pp.1607-1637.
[8] Saad, W., Bennis, M. and Chen, M., 2019. A vision of 6G wireless systems: Applications, trends, technologies, and open research problems. IEEE network, 34(3), pp.134-142.
[9] Nguyen, D.C., Ding, M., Pathirana, P.N., Seneviratne, A., Li, J., Niyato, D., Dobre, O. and Poor, H.V., 2021. 6G Internet of Things: A comprehensive survey. IEEE Internet of Things Journal, 9(1), pp.359-383.
[10] Bast, H., Delling, D., Goldberg, A., Müller-Hannemann, M., Pajor, T., Sanders, P., Wagner, D. and Werneck, R.F., 2016. Route planning in transportation networks. In Algorithm engineering: Selected results and surveys (pp. 19-80). Cham: Springer International Publishing.
[11] Nazari, M., Oroojlooy, A., Snyder, L. and Takác, M., 2018. Reinforcement learning for solving the vehicle routing problem. Advances in neural information processing systems, 31.
[12] Kool, W., Van Hoof, H. and Welling, M., 2018. Attention, learn to solve routing problems!. arXiv preprint arXiv:1803.08475.
[13] Yu, B., Yin, H. and Zhu, Z., 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875.
[14] Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K. and Zhang, J., 2019. Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE, 107(8), pp.1738-1762.
[15] Wei, H., Zheng, G., Yao, H. and Li, Z., 2018, July. Intellilight: A reinforcement learning approach for intelligent traffic light control. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 2496-2505).
[16] Samarakoon, S., Bennis, M., Saad, W. and Debbah, M., 2018, December. Federated learning for ultra-reliable low-latency V2V communications. In 2018 IEEE global communications conference (GLOBECOM) (pp. 1-7). IEEE.
[17] Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P. and Bengio, Y., 2018, February. Graph attention networks. In International conference on learning representations (Vol. 6, p. 2).
[18] Zhang, J., Zheng, Y. and Qi, D., 2017, February. Deep spatio-temporal residual networks for citywide crowd flows prediction. In Proceedings of the AAAI conference on artificial intelligence (Vol. 31, No. 1).
[19] Fuller, A., Fan, Z., Day, C. and Barlow, C., 2020. Digital twin: Enabling technologies, challenges and open research. IEEE access, 8, pp.108952-108971.
[20] Mozaffari, M., Saad, W., Bennis, M., Nam, Y.H. and Debbah, M., 2019. A tutorial on UAVs for wireless networks: Applications, challenges, and open problems. IEEE communications surveys & tutorials, 21(3), pp.2334-2360.
[21] Saad, W., Bennis, M. and Chen, M., 2019. A vision of 6G wireless systems: Applications, trends, technologies, and open research problems. IEEE network, 34(3), pp.134-142.
[22] Pandya, S., Srivastava, G., Jhaveri, R., Babu, M.R., Bhattacharya, S., Maddikunta, P.K.R., Mastorakis, S., Piran, M.J. and Gadekallu, T.R., 2023. Federated learning for smart cities: A comprehensive survey. Sustainable Energy Technologies and Assessments, 55, p.102987.
[23] Al-Huthaifi, R., Li, T., Huang, W., Gu, J. and Li, C., 2023. Federated learning in smart cities: Privacy and security survey. Information Sciences, 632, pp.833-857.
[24] Maroua, D., 2024. A state-of-the-art on federated learning for vehicular communications. Vehicular Communications, 45, p.100709.
[25] Li, J., Zheng, C. and Chen, Z., 2025. Resilient Federated Learning for Vehicular Networks: A Digital Twin and Blockchain-Empowered Approach. Future Internet, 17(11), p.505.
[26] Asad, M., Shaukat, S., Nakazato, J., Javanmardi, E. and Tsukada, M., 2025. Federated learning for secure and efficient vehicular communications in open RAN. Cluster Computing, 28(3), p.211.
[27] Lis, M. and Mądziel, M., 2026. Green Transportation Planning for Smart Cities: Digital Twins and Real-Time Traffic Optimization in Urban Mobility Networks. Applied Sciences, 16(2), p.678..
[28] Yessef, M., Hakam, Y., Tabaa, M., Alammar, M.M. and Elbarbary, Z.M., 2025. Digital twin technology in smart cities: A step toward intelligent urban management. Energy Reports, 14, pp.5539-5557.
[29] Behrisch, M., Bieker, L., Erdmann, J. and Krajzewicz, D., 2011. SUMO–simulation of urban mobility: an overview. In Proceedings of SIMUL 2011, the third international conference on advances in system simulation. ThinkMind.
[30] Gong, L. and Ding, M., 2025. Urban traffic digital twin system development in unity. Scientific Reports, 15(1), p.40085.
[31] Abdallah, W. and Alghamdi, M., 2026. Digital twin-enabled AI for sustainable traffic management: real-time urban mobility optimization in smart cities. PeerJ Computer Science, 12, p.e3574.
[32] Salwasser, D., Seemaier, D., Gottesbüren, L. and Sanders, P., 2025, June. Tera-scale multilevel graph partitioning. In 2025 IEEE International Parallel and Distributed Processing Symposium (IPDPS) (pp. 285-296). IEEE.
[33] Gilbert, M.S., Madduri, K., Boman, E.G. and Rajamanickam, S., 2023. Jet: Multilevel graph partitioning on GPUs. arXiv preprint arXiv:2304.13194.
[34] Gottesbüren, L., Dhulipala, L., Jayaram, R. and Lacki, J., 2024. Unleashing graph partitioning for large-scale nearest neighbor search. arXiv preprint arXiv:2403.01797.
[35] Bast, H., Delling, D., Goldberg, A., Müller-Hannemann, M., Pajor, T., Sanders, P., Wagner, D. and Werneck, R.F., 2016. Route planning in transportation networks. In Algorithm engineering: Selected results and surveys (pp. 19-80). Cham: Springer International Publishing.
[36] Sanders, P. and Seemaier, D., 2023, August. Distributed deep multilevel graph partitioning. In European conference on parallel processing (pp. 443-457). Cham: Springer Nature Switzerland.
[37] Zanella, A., Bui, N., Castellani, A., Vangelista, L. and Zorzi, M., 2014. Internet of things for smart cities. IEEE Internet of Things journal, 1(1), pp.22-32.
[38] Barceló, J., 2010. Fundamentals of traffic simulation (Vol. 145, p. 439). New York: Springer.
[39] Lv, Y., Duan, Y., Kang, W., Li, Z. and Wang, F.Y., 2014. Traffic flow prediction with big data: A deep learning approach. Ieee transactions on intelligent transportation systems, 16(2), pp.865-873.
[40] Shi, W., Cao, J., Zhang, Q., Li, Y. and Xu, L., 2016. Edge computing: Vision and challenges. IEEE internet of things journal, 3(5), pp.637-646.
[41] Chu, T., Wang, J., Codecà, L. and Li, Z., 2019. Multi-agent deep reinforcement learning for large-scale traffic signal control. IEEE transactions on intelligent transportation systems, 21(3), pp.1086-1095.
[42] Kairouz, P. and McMahan, H.B., 2021. Advances and open problems in federated learning. Foundations and trends in machine learning, 14(1-2), pp.1-210.
[43] Doshi-Velez, F. and Kim, B., 2017. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
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