Smart City Gnosys
Smart city article details
| Title | Impact Of Iot Data Integration On Real-Time Analytics For Smart City Management |
|---|---|
| ID_Doc | 30379 |
| Authors | Lakshmi D.; Jeyarani J.; Suguna R.; Muneeshwari P.; Valantina G.M.; Jayaraman S. |
| Year | 2024 |
| Published | Proceedings of the 2024 10th International Conference on Communication and Signal Processing, ICCSP 2024 |
| DOI | http://dx.doi.org/10.1109/ICCSP60870.2024.10543926 |
| Abstract | The paper in illustration will emphasize the necessity for an IoT big data and analytics on time fusion as a futuristic smart city management approach. The framework deals with the main problems observed in data assimilation, analytics, security and scalability. As a result, cities will enjoy the services and operations utilization of the urban data with the precision. Our approach is based upon the data collection with the use of the edge technologies, preprocessing, integration, and analysis of the data resulting in the creation of the tools of machine learning and the adoption of interoperability standards. The results have especially brought to the limelight the fantastic performance of the live analytics pipeline when considered as a whole, and the impressive throughput of 5,000 events per second alongside the very low latency of 50 milliseconds, indicating how the urban information flow is processed in the blink of an eye. With AI therein creating the precision rate of 88.7%-95.2%, which therefore means this level of an urban situation can pronounce a traffic congestion and air quality fluctuations. The situation where security systems that involve encryption, access controls, and privacy-preserving protocols, among other mechanisms, have been used for the management of urban data in a safe manner, away from any unauthorized people and hackers, is factual. For the sake of achieving scalability, the replacement of certain bottlenecks is applied in the system by means of horizontal scaling and optimal performance procedures assisting smart cities handle their increasing quantity of data. Usually the results are factorized to bring out the fact that the particular methodology is strong and thus can be used in real practice to transform and enhance urban resilience level. © 2024 IEEE. |
| Author Keywords | IOT Data Integration; Machine learning tools; Realtime analysis; smart city management |
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