Enhancing Smart City Services Using Real-Time Big Data Analytics
DOI:
https://doi.org/10.5281/zenodo.19918230Keywords:
smart city, big data, new technologyAbstract
This research explores how real-time big data analytics can improve smart city services, particularly in traffic and environmental monitoring. It proposes an integrated system using Apache Kafka, Spark, and IoT sensors to collect, analyze, and visualize live data. Through literature review and proposed implementation framework, the study highlights how these technologies can optimize traffic flow, reduce pollution, and enhance decision-making. The research identifies key challenges in real-time analytics and suggests solutions for future smart city implementations.
References
Batty, M., Axhausen, K. W., Giannotti, F., et al. (2012). Smart cities of the future. The European Physical Journal Special Topics, 214(1), 481–518.
Beevers, S., Westmoreland, E., & Carslaw, D. (2012). Trends in NOx and NO2 emissions from road traffic in Great Britain. Science of The Total Environment, 441, 202–211.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.
Google AI Blog. (2023). Green Light: AI for Smarter Traffic Signals. https://ai.googleblog.com
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2016). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 53, 98-115. (While not exclusively smart cities, it's a foundational big data paper relevant to the context).
IRJMETS (International Research Journal of Modern Engineering & Technology Sciences) (2024). REAL-TIME DATA ANALYTICS FOR SMART CITIES: OPTIMIZING URBAN INFRASTRUCTURE AND RESOURCE MANAGEMENT. International Research Journal of Modern Engineering & Technology Sciences (IRJMETS), 6(09).
Khan, N., Singh, V. P., & Singh, A. K. (2020). Smart Waste Management in Smart Cities Using IoT and Big Data Analytics: A Review. International Journal of Advanced Science and Technology, 29(03), 196-204.
Le, T., & Cha, S. (2019). Deep learning for PM2.5 forecasting based on traffic and meteorological data. arXiv preprint arXiv:1911.12919.
N.Shehata and A.Abed, “Big Data with Column Oriented NOSQL Database to Overcome the Drawbacks of Relational Databases,” Int. J. Adv. Netw. Appl., vol. 4428, pp. 4423–4428, 2020.
Nguyen, H. T., Tran, T. T., & Le, V. (2021). Traffic-based pollution monitoring using deep learning models. IEEE Access, 9, 154921-154933.
Olaniyi, O. O., Okunleye, O. J., & Olabanji, S. O. (2023). Advancing data-driven decision-making in smart cities through big data analytics: A comprehensive review of existing literature. Current Journal of Applied Science and Technology, 42(25), 10-18.
Osman, A. M. S., & Elragal, A. (2021). Smart cities and big data analytics: a data-driven decision-making use case. Smart Cities, 4(1), 286-313.
Seervi, U. (2022). Real-Time Smart City Monitoring System. GitHub repository.
Smith, S., Barlow, G., et al. (2013). Adaptive traffic signal control with real-time traffic prediction. Transportation Research Board Annual Meeting.
Toshniwal, A., Taneja, S., Shukla, A., et al. (2014). Storm@Twitter. In Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data (pp. 147–156).
Zhang, Y., Cheng, L., & Boutaba, R. (2019). Traffic prediction in smart cities: A deep learning-based approach. IEEE Network, 33(2), 164-171.
Zhou, Y., Cao, J., & Wu, L. (2017). Big data privacy in smart cities. Security and Privacy, 1(2), e18.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Naglaa Saeed Shehata (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright. Licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/ (CC BY 4.0 deed)
