Fusion of AI and IoT for Predictive Maintenance in Smart Cities
DOI:
https://doi.org/10.22105/scfa.v1i2.37Keywords:
Artificial intelligence, Internet of things, Predictive maintenance, Smart cities, Real-time monitoring, Urban infrastructureAbstract
Predictive maintenance, an anticipatory method for managing assets, has seen remarkable growth in recent times. The combination of Artificial Intelligence (AI) and Internet of Things (IoT) technologies offers a significant shift in this area. This research paper investigates the collaborative potential of AI and IoT in facilitating predictive maintenance within smart city frameworks. By utilizing the extensive data produced by IoT sensors alongside the analytical power of AI algorithms, it's feasible to foresee equipment breakdowns, improve maintenance timelines, and boost overall system dependability. This paper examines the essential elements of predictive maintenance systems that are based on AI and IoT, such as data collection, feature development, model training, and prediction creation. Furthermore, it addresses the obstacles and possibilities related to the implementation of these systems in urban settings. Through a detailed review of existing literature and practical examples, this paper seeks to offer meaningful insights into the latest advancements and future pathways in AI-IoT-based predictive maintenance for smart cities.
References
[1] Rehan, H. (2023). Internet of things (IoT) in smart cities: enhancing urban living through technology. Journal of engineering and technology, 5(1), 1–16. https://www.researchgate.net/publication/383611526
[2] 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. https://doi.org/10.1007/s41870-022-00872-6
[3] Pratap, A., Nayan, H., Panda, P., & Mohapatra, H. (2024). Emerging technologies and trends in the future of smart cities and IoT: A review. Journal of network security computer networks, 10(2), 28–38. https://matjournals.net/engineering/index.php/JONSCN/article/view/606
[4] Al-Turjman, F. (2019). Artificial intelligence in IoT. Springer. https://link.springer.com/book/10.1007/978-3-030-04110-6
[5] Shahin, M., Chen, F. F., Hosseinzadeh, A., & Zand, N. (2023). Using machine learning and deep learning algorithms for downtime minimization in manufacturing systems: an early failure detection diagnostic service. The international journal of advanced manufacturing technology, 128(9), 3857–3883. https://doi.org/10.1007/s00170-023-12020-w
[6] Zeng, F., Pang, C., & Tang, H. (2024). Sensors on internet of things systems for the sustainable development of smart cities: A systematic literature review. Sensors, 24(7), 2074. https://doi.org/10.3390/s24072074
[7] Ray, S. (2019). A quick review of machine learning algorithms. 2019 international conference on machine learning, big data, cloud and parallel computing (COMITCON) (pp. 35–39). IEEE. https://doi.org/10.1109/COMITCon.2019.8862451
[8] Noshad, Z., Javaid, N., Saba, T., Wadud, Z., Saleem, M. Q., Alzahrani, M. E., & Sheta, O. E. (2019). Fault detection in wireless sensor networks through the random forest classifier. Sensors, 19(7), 1568. https://doi.org/10.3390/s19071568
[9] Ghosh, S., Dasgupta, A., & Swetapadma, A. (2019). A study on support vector machine based linear and non-linear pattern classification. 2019 international conference on intelligent sustainable systems (ICISS) (pp. 24–28). IEEE. https://doi.org/10.1109/ISS1.2019.8908018
[10] Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Umar, A. M., Linus, O. U., … Kiru, M. U. (2019). Comprehensive review of artificial neural network applications to pattern recognition. IEEE access, 7, 158820–158846. https://doi.org/10.1109/ACCESS.2019.2945545
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