Research Article
AI IoT-Powered Smart City Energy Management Systems: A Framework for Efficient Resource Management
Downloads
Abstract
This paper introduces a scalable, integrated Artificial Intelligence (AI)-driven Internet of Things (IoT) framework to optimize resource management in smart city infrastructures, particularly for water, energy, waste, and transportation. As urban populations grow, the demands for efficient resource distribution and waste management intensify, requiring systems that can process and react to data in real time. The proposed framework incorporates a layered IoT system architecture, scalability features, advanced data processing algorithms, and security measures to handle large-scale IoT device deployments and data flows in urban settings. Tested against existing systems, the framework demonstrates substantial improvements in resource optimization and efficiency. Performance metrics, comparative analysis, and security evaluations underscore the framework’s robustness and reliability, supporting sustainable smart city growth.
Keywords
Smart city Artificial intelligence Internet of things Resource management Scalability Urban infrastructureReferences (17)
- [1] Mohanty, S. P., Choppali, U., & Kougianos, E. (2016). Everything you wanted to know about smart cities: The internet of things is the backbone. IEEE Consumer Electronics Magazine, 5(3), 60–70. https://doi.org/10.1109/MCE.2016.2556879
- [2] Khatoun, R., & Zeadally, S. (2016). Smart cities: Concepts, architectures, research opportunities. Communications of the Acm, 59(8), 46–57. https://doi.org/10.1145/2858789
- [3] Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of things for smart cities. IEEE Internet of Things Journal, 1(1), 22–32. https://doi.org/10.1109/JIOT.2014.2306328
- [4] Solanas, A., Patsakis, C., Conti, M., Vlachos, I. S., Ramos, V., Falcone, F., ... & Martinez-Balleste, A. (2014). Smart health: A context-aware health paradigm within smart cities. IEEE Communications Magazine, 52(8), 74–81. https://doi.org/10.1109/MCOM.2014.6871673
- [5] Sheth, A., Henson, C., & Sahoo, S. S. (2008). Semantic sensor web. IEEE Internet Computing, 12(4), 78–83. https://doi.org/10.1109/MIC.2008.87
- [6] Sheng, Z., Yang, S., Yu, Y., Vasilakos, A., McCann, J., & Leung, K. (2013). A survey on the ietf protocol suite for the internet of things: Standards, challenges, and opportunities. IEEE Wireless Communications, 20(6), 91–98. https://doi.org/10.1109/MWC.2013.6704479
- [7] Hou, X., Li, Y., Chen, M., Wu, D., Jin, D., & Chen, S. (2016). Vehicular fog computing: A viewpoint of vehicles as the infrastructures. IEEE Transactions on Vehicular Technology, 65(6), 3860–3873. https://doi.org/10.1109/TVT.2016.2532863
- [8] Zhou, X., Ke, R., Yang, H., & Liu, C. (2021). When intelligent transportation systems sensing meets edge computing: Vision and challenges. Applied Sciences, 11(20), 9680. https://doi.org/10.3390/app11209680
- [9] Mocanu, E., Nguyen, P. H., Gibescu, M., & Kling, W. L. (2016). Deep learning for estimating building energy consumption. Sustainable Energy, Grids and Networks, 6, 91–99. https://doi.org/10.1016/j.segan.2016.02.005
- [10] Bengio, Y. (2009). Learning deep architectures for AI. Foundations and Trends®In Finance, 2(1), 1–127. https://doi.org/10.1561/2200000006
- [11] Ma, C., Zhou, J., Xu, X., & Xu, J. (2020). Evolution regularity mining and gating control method of urban recurrent traffic congestion: A literature review. Journal of Advanced Transportation, 2020(1), 5261580. https://doi.org/10.1155/2020/5261580
- [12] Cartella, F., Lemeire, J., Dimiccoli, L., & Sahli, H. (2015). Hidden semi-markov models for predictive maintenance. Mathematical Problems in Engineering, 2015(1), 278120. https://doi.org/10.1155/2015/278120
- [13] Cardone, G., Foschini, L., Bellavista, P., Corradi, A., Borcea, C., Talasila, M., & Curtmola, R. (2013). Fostering participaction in smart cities: A geo-social crowdsensing platform. IEEE Communications Magazine, 51(6), 112–119. https://doi.org/10.1109/MCOM.2013.6525603
- [14] Institut Municipal d’Informàtica (Barcelona). (2021). Smart city expo world congress 2021. https://ajuntament.barcelona.cat/institut-innovacio-tecnologia//sites/default/files/2024-01/relat_scewc_2021_eng_1.pdf
- [15] Al-Turjman, F., & Abujubbeh, M. (2019). IoT-enabled smart grid via SM: An overview. Future Generation Computer Systems, 96, 579–590. https://doi.org/10.1016/j.future.2019.02.012
- [16] Smart Nation and Digital Government Office. (2018). Smart nation: The way forward: executive summary. https://www.smartnation.gov.sg/
- [17] Noori, N., Hoppe, T., & de Jong, M. (2020). Classifying pathways for smart city development: Comparing design, governance and implementation in Amsterdam, Barcelona, Dubai, and Abu Dhabi. Sustainability, 12(10), 4030. https://doi.org/10.3390/su12104030