Smart City Gnosys

Smart city article details

Title	Real-Time Analytics For Big Data In Smart City Applications: Transforming Urban Environments With Data-Driven Insights
ID_Doc	44300
Authors	Khanderi A.; Rajaram G.; Hussein R.R.; Johri P.; Mohanraj T.; Balamurugan M.
Year	2025
Published	2025 International Conference on Automation and Computation, AUTOCOM 2025
DOI	http://dx.doi.org/10.1109/AUTOCOM64127.2025.10956868
Abstract	This paper offers an overview of real-time analytics for smart cities proposing a framework for real-time data processing to improve the urban landscape. In the proposed system, large-scale data-streams can be processed using advanced machine learning algorithms, edge computing, and distributed data storage. Analyzing the outcomes of the evaluation procedure evidences reportable performance enhancements in the areas of smart city applications. Average traffic speed in transportation systems in the same period rose by 60%, concurrency and response to incidents likewise cut down by 40% and 53% respectively. The energy management systems responded with a 16% decrease in maximum energy load and the waste reduction was by 50%, while the costs savings was by 25% increase. Public safety applications alone realised a 60% decrease in the time taken to detect a crime and a 28.6% increase in efficiency on disaster resources with an overall spike in public satisfaction of 25.7%. Such findings improve the understanding of how this framework can increase urban efficiency, sustainability, and quality of life, which makes the solution usable in future smart city projects. © 2025 IEEE.
Author Keywords	Big Data; Congestion Reduction; Data-Driven Insights; Energy Demand Reduction; Performance Metrics; Real-Time Analytics; Smart City; Sustainable Urban Development; Traffic Speed; Urban Environment

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