IoT-Driven Smart Urban Infrastructure Monitoring and Predictive Maintenance Using Artificial Intelligence
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Abstract
The rapid growth of urban populations and infrastructure complexity has created significant challenges in maintaining the safety, reliability, and efficiency of urban systems. This study proposes an IoT-driven smart infrastructure monitoring and predictive maintenance framework integrated with artificial intelligence (AI) and real-time analytics. The research analyzes the contribution of different monitoring components, including structural sensors (30%), traffic sensors (22%), environmental sensors (18%), energy monitoring devices (16%), and vibration/acoustic sensors (14%). The implementation of intelligent systems demonstrates notable improvements in predictive maintenance accuracy (35%), fault detection (33%), response time (31%), cost efficiency (28%), and operational reliability (27%). The proposed system utilizes a multi-layered architecture comprising sensing, communication, processing, and application layers. Machine learning algorithms are applied to analyze infrastructure data, detect anomalies, and predict potential failures. Edge and cloud computing technologies enhance system performance by enabling real-time processing and scalable data management. The findings highlight the effectiveness of IoT and AI integration in improving infrastructure monitoring and maintenance. The proposed framework supports proactive decision-making, reduces operational risks, and enhances urban sustainability. This research contributes to the development of smart city infrastructures and demonstrates the potential of intelligent systems in modern urban management.
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References
[1] L. Atzori, A. Iera, and G. Morabito, “The internet of things: A survey,” Comput. networks, vol. 54, no. 15, pp. 2787–2805, 2010.
[2] N. Vanu, M. R. Hasan, T. R. Sikder, and Z. S. Tamanna, “AI-Driven Big Data Analytics for Precision Medicine: A Unified Framework Integrating Molecular Data Intelligence, Wearable Health Systems, and Predictive Modeling,” J. Comput. Sci. Technol. Stud., vol. 3, no. 2, pp. 124–141, 2021.
[3] J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of Things (IoT): A vision, architectural elements, and future directions,” Futur. Gener. Comput. Syst., vol. 29, no. 7, pp. 1645–1660, 2013.
[4] S. Nusrat, F. Hossain, and T. R. Sikder, “Integrating Wearable Health Data and Environmental Management Analytics for AI-Driven Cardiovascular Disease Prevention,” Eastasouth J. Inf. Syst. Comput. Sci., vol. 2, no. 02, pp. 209–223, 2024.
[5] B. J. A. Juie, J. U. Z. Kabir, R. A. Ahmed, and M. M. Rahman, “Evaluating the impact of telemedicine through analytics: Lessons learned from the COVID-19 era,” J. Med. Heal. Stud., vol. 2, no. 2, pp. 161–174, 2021.
[6] M. I. Alam, M. A. K. P. Hemal, M. A. Sami, and M. L. Rahman, “Robust and Interpretable Crop Recommendation: A Case Study on a Balanced Multi-crop Agronomic Dataset,” Eur. J. Ecol. Biol. Agric., vol. 1, no. 5 SE-Articles, pp. 168–184, Nov. 2024, doi: 10.59324/ejeba.2024.1(5).14.
[7] T. R. Sikder, M. A. Siam, M. M. H. Melon, S. M. M. Uddin, S. C. Mohonta, and F. Karim, “A Multimodal Data Analytics Framework for Early Cancer Detection Using Genomic, Radiomic, and Clinical Big Data Fusion,” J. Comput. Sci. Technol. Stud., vol. 5, no. 3, pp. 183–188, 2023.
[8] W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge computing: Vision and challenges,” IEEE internet things J., vol. 3, no. 5, pp. 637–646, 2016.
[9] T. R. Sikder, S. Dash, B. Uddin, and F. Hossain, “AI-Powered Data Analytics and Multi-Omics Integration for Next-Generation Precision Oncology and Anticancer Drug Development,” Eastasouth J. Inf. Syst. Comput. Sci., vol. 1, no. 02 SE-Articles, pp. 153–170, Dec. 2023, doi: 10.58812/esiscs.v1i02.838.
[10] M. A. Sami, M. A. K. P. Hemal, M. I. Alam, and M. L. Rahman, “Data Governance and Analytics Infrastructure for Scalable Decision-Making in Development and Agritech Programs,” Eur. J. Appl. Sci. Eng. Technol., vol. 2, no. 2, pp. 388–403, 2024.
[11] S. Sicari, A. Rizzardi, L. A. Grieco, and A. Coen-Porisini, “Security, privacy and trust in Internet of Things: The road ahead,” Comput. networks, vol. 76, pp. 146–164, 2015.
[12] Md. Ishtiaque Alam, Mohammad Abdus Sami, Md Abu Kawsar Prodhan Hemal, and Md Lutfor Rahman, “Predictive Analytics and Decision Intelligence for Climate-Resilient Agritech Systems,” Acad. Glob. J. Comput. Sci. Technol. Stud., vol. 2, no. 1 SE-Research Article, pp. 44–56, 2023, doi: 10.32996/agjcsts.2023.2.1.4.
[13] F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, “Fog computing and its role in the internet of things,” in Proceedings of the first edition of the MCC workshop on Mobile cloud computing, 2012, pp. 13–16.
[14] C. Zhang, P. Patras, and H. Haddadi, “Deep learning in mobile and wireless networking: A survey,” IEEE Commun. Surv. tutorials, vol. 21, no. 3, pp. 2224–2287, 2019.