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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM63518

SMART CITY COMMAND HUB USING BIG DATA DIGITAL AND AI

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63518

Volume:

10 |

5 |

May 2026

Pages:

1-5

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM63518

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

UMESH HANGARAGI

UMESH HANGARAGI

MASTER OF COMPUTER APPLICATIONS

BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY DAVANAGERE

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JKR

JYOTHIKA K R

MASTER OF COMPUTER APPLICATIONS

BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY DAVANAGERE

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JKR

JYOTHIKA K R

MASTER OF COMPUTER APPLICATIONS

BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY DAVANAGERE

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Abstract

This project introduces a comprehensive Smart City Command Hub, a centralized platform. designed to. revolutionize urban governance by leveraging the synergistic power of Big Data, Digital Twin technology, and Artificial Intelligence. (AI). and predictive operational view of the city's multifaceted systems. By aggregating and analysing vast streams of data from a diverse array of IoT sensors, existing city infrastructure, and public feedback channels, the Command Hub will provide city administrators and planners with unprecedented situational awareness and actionable insights. The integration of a dynamic Digital Twin model will offer a virtual, interactive, and constantly evolving replica of the city's physical assets and systems, enabling immersive visualization, complex scenario simulation, and proactive infrastructure management. AI engine that the optimization of city services. This will. facilitate a paradigm shift from reactive problem-solving to proactive and data-driven decision-making in critical domains such as traffic management, public safety, utility distribution, environmental monitoring, and emergency response. The Smart City Command Hub aims to break down the silos of traditional city management, fostering inter-departmental collaboration and a more citizen-centric approach to governance. Ultimately, this project endeavours to a more responsive, resilient, and sustainable city for its inhabitant.

KEYWORDS

Digital And AI

Cite This Article

UMESH HANGARAGI, JYOTHIKA K R, JYOTHIKA K R, "SMART CITY COMMAND HUB USING BIG DATA DIGITAL AND AI," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-5, May 2026. https://doi.org/10.55041/IJSREM63518

Full Article/Conclusion

10] The "Smart City Command Hub Using Big Data, Digital Twin, and AI" project represents a bold and necessary step forward urban governance. In an increasingly urbanized. world, the challenges facing and interconnected. The traditional, siloed approach to city management that will empower city leaders with the tools and insights they need to build more efficient, sustainable, and resilient cities. By harnessing the power of Big Data, we can move beyond anecdotal evidence and make decisions based on a comprehensive and real-time understanding of our urban environments. The integration of a dynamic  Digital Twin will provide an unprecedented ability to. visualize, simulate, and plan for the future, enabling us to design cities that are more liveable and better equipped to handle the challenges of growth and. change. And with the application of Artificial Intelligence, we can unlock the predictive and prescriptive. power of our data, moving from a reactive to a proactive mode of governance. The journey to building a fully integrated Smart City Command Hub will not be without its challenges. It will require significant investment, . strong . political will, and a culture of collaboration across all city departments. We must also be mindful of the ethical implications of collecting and using large amounts of citizen data, ensuring that privacy is protected and that all residents.

References

1. McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Google AI. 2. Sheller, M. J., Edwards, B., Reina, G. A., et al. (2020). Federated learning in medicine: Enabling multi-institutional collaborations. Nature Scientific Reports. 3. Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine. 4. Teo, J. W., Tan, J., & Lim, S. (2024). Systematic review of federated learning applications in healthcare. PMC. 5. Zhao, R., Li, X., & Wang, Y. (2023). Explainable federated learning for smart healthcare. PubMed. 6. Eden, H., et al. (2025). Governance mechanisms for federated learning in healthcare. Nature Digital Medicine. 7. Madathil, K. C., et al. (2024). Revolutionizing healthcare data analytics with federated learning. ScienceDirect. 8. Zhang, Y., et al. (2023). Recent methodological advances in federated learning for healthcare. ScienceDirect. 9. Pati, S., et al. (2024). Privacy preservation techniques for federated learning in healthcare. ScienceDirect. 10. Abbas, S., et al. (2025). Federated learning applications in smart healthcare systems. PubMed.

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Publication Date

5/20/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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