Fog Computing and AI for Real-time Disaster Response in Smart Cities
Keywords:
Fog computing, Artificial intelligence, Real-time disaster response, Smart city, IoT, Edge computingAbstract
The swift advancement of smart cities requires effective disaster response systems. This study explores how Fog Computing and Artificial Intelligence (AI) contribute to improving real-time disaster management, with fog computing facilitating rapid, edge-level data processing, while AI aids in predictive analytics and decision-making. Through examples in flood forecasting, earthquake surveillance, and fire detection, we demonstrate the successful implementation of these technologies in smart cities. By addressing existing challenges and looking toward future developments, this research emphasizes the capability of fog computing and AI to establish robust and adaptive frameworks for urban disaster response.
References
Picioroaga, I., Eremia, M., & Mihai, S. (2018). SMART city: Definition and evaluation of key performance indicators [presentation]. Conference: 2018 international conference and exposition on electrical and power engineering (EPE) (pp. 217–222). http://dx.doi.org/10.1109/ICEPE.2018.8559763
Chowdhury, S. N., Dhawan, S., & Agnihotri, A. (2016). Crowd-sourcing for smart cities. 2016 IEEE international conference on recent trends in electronics, information & communication technology (RTEICT), (pp. 360–365. IEEE. https://doi.org/10.1109/RTEICT.2016.7807842
Lenka, R. K., Kolhar, M., Mohapatra, H., Al-Turjman, F., & Altrjman, C. (2022). Cluster-based routing protocol with static hub (CRPSH) for WSN-assisted IoT networks. Sustainability, 14(12). https://doi.org/10.3390/su14127304
Khalid Dar, B., Shah, M., & Shahid, H. (2018). Fog computing based automated accident detection and emergency response system using android smartphone [presentation]. 2018 14th international conference on emerging technologies (ICET) (pp. 1–6). http://dx.doi.org/10.1109/ICET.2018.8603557
Punuri, S. B., Kuanar, S. K., Kolhar, M., Mishra, T. K., Alameen, A., Mohapatra, H., & Mishra, S. R. (2023). Efficient Net-XGBoost: An implementation for facial emotion recognition using transfer learning. Mathematics, 11(3). https://doi.org/10.3390/math11030776
Sree, T. (2021). A framework for disaster monitoring using fog computing. In Congress on intelligent systems (pp. 485–493). Advances in Intelligent Systems and Computing. http://dx.doi.org/10.1007/978-981-33-6984-9_39
Hadiana, A. (2020). Fog computing architecture for indoor disaster management. International journal of informatics, information system and computer engineering (injiiscom), 1(1), 79–90.
Sünbül, G., & Soyluk, A. (2024). A review of using deep learning technology in the built environment of disaster management phasesyapılı çevrede afet yönetimi aşamalarında derin öğrenme teknolojisinin kullanımına ilişkin bir inceleme. Mimarlık bilimleri VE uygulamaları dergisi (MBUD), 201–218. http://dx.doi.org/10.30785/mbud.1333736
Aboualola, M., Abualsaud, K., Khattab, T., Zorba, N., & Hassanein, H. S. (2023). Edge technologies for disaster management: A survey of social media and artificial intelligence integration. IEEE access, 11, 73782–73802. https://doi.org/10.1109/ACCESS.2023.3293035
Puliafito, C., Mingozzi, E., Longo, F., Puliafito, A., & Rana, O. (2019). Fog computing for the internet of things: A survey. ACM transactions on internet technology (TOIT), 19(2), 1–41. https://doi.org/10.1145/3301443
Mukherjee, M., Shu, L., & Wang, D. (2018). Survey of fog computing: Fundamental, network applications, and research challenges. IEEE communications surveys & tutorials, 20(3), 1826–1857. https://doi.org/10.1109/COMST.2018.2814571
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