AI-Driven Routing Algorithms for IoT Enabled Smart-City Infrastructure
Abstract
As urban infrastructure continues to develop towards increased interconnectivity, artificial intelligence (AI) has become a key enabler for enhancing IoT-integrated smart city systems. AI-based routing algorithms are essential in processing the vast quantities of data produced by IoT devices, leading to more efficient, adaptive, and durable urban services. These algorithms continually process and evaluate real-time information from connected sensors and devices, allowing for optimized routing in various applications such as traffic management, emergency response, waste collection, and energy distribution. Utilizing machine learning, reinforcement learning, and predictive analytics, AI-enhanced routing systems improve the agility and sustainability of urban infrastructure. This paper explores different AI-powered routing models and methods, examines their integration within IoT systems, and discusses issues related to data privacy, security, and scalability. In summary, AI-driven routing improves smart city infrastructure by delivering quicker, more intelligent, and adaptable solutions, which are crucial for cities looking to enhance resource utilization, decrease congestion, and foster a better quality of urban life.
Keywords:
AI, Routing algorithms, Smart cities, Machine learningReferences
- [1] Goudarzi, A., Ghayoor, F., Waseem, M., Fahad, S., & Traore, I. (2022). A survey on IoT-enabled smart grids: emerging, applications, challenges, and outlook. Energies, 15(19), 6984. https://doi.org/10.3390/en15196984
- [2] Kirmani, S., Mazid, A., Khan, I. A., & Abid, M. (2022). A survey on IoT-enabled smart grids: technologies, architectures, applications, and challenges. Sustainability, 15(1), 717. https://doi.org/10.3390/su15010717
- [3] Abir, S. M. A. A., Anwar, A., Choi, J., & Kayes, Asm. (2021). Iot-enabled smart energy grid: Applications and challenges. IEEE access, 9, 50961–50981. https://doi.org/10.1109/ACCESS.2021.3067331
- [4] Chi, H. R., & Radwan, A. (2020). Multi-objective optimization of green small cell allocation for IoT applications in smart city. IEEE access, 8, 101903–101914. https://ieeexplore.ieee.org/abstract/document/9099810
- [5] Mohapatra, H., Rath, A. K., & Panda, N. (2022). IoT infrastructure for the accident avoidance: an approach of smart transportation. International journal of information technology, 14(2), 761–768.
- [6] Mohapatra, H., Kolhar, M., & Dalai, A. K. (2024). Efficient energy management by using sjf scheduling in wireless sensor network [presentation]. International conference on advances in distributed computing and machine learning (pp. 211–221). https://doi.org/10.1007/978-981-97-1841-2_15
- [7] Adeyinka, K. I., & Adeyinka, T. I. (2025). Real-Time Traffic Management Using Graph Models. In Neural networks and graph models for traffic and energy systems (pp. 231–258). IGI Global Scientific Publishing. https://www.igi-global.com/chapter/real-time-traffic-management-using-graph-models/370938
- [8] Zhou, S., Chen, X., Li, C., Chang, W., Wei, F., & Yang, L. (2024). Intelligent road network management supported by 6G and deep reinforcement learning. IEEE transactions on intelligent transportation systems. https://doi.org/10.1109/TITS.2024.3451193
- [9] Alotaibi, I., Abido, M. A., Khalid, M., & Savkin, A. V. (2020). A comprehensive review of recent advances in smart grids: A sustainable future with renewable energy resources. Energies, 13(23), 6269.
- [10] Li, D., Zhang, Z., Alizadeh, B., Zhang, Z., Duffield, N., Meyer, M. A., … Behzadan, A. H. (2024). A reinforcement learning-based routing algorithm for large street networks. International journal of geographical information science, 38(2), 183–215. https://doi.org/10.1080/13658816.2023.2279975
- [11] Yue, B., Ma, J., Shi, J., & Yang, J. (2024). A deep reinforcement learning-based adaptive search for solving time-dependent green vehicle routing problem. IEEE access. https://doi.org/10.1109/ACCESS.2024.3369474
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