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ARTICLE
ACCELERATING URBAN INTELLIGENCE: A FRAMEWORK FOR REAL-TIME EDGE ANALYTICS IN IOT-DRIVEN SMART CITIES
Issue Vol. 1 No. 01 (2024): Volume 01 Issue 01 --- Section Articles
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Abstract
The proliferation of Internet of Things (IoT) devices within urban environments has heralded the era of the smart city, promising enhanced efficiency, sustainability, and quality of life. However, the unprecedented volume of data generated by these devices poses significant challenges to traditional cloud-centric data processing models, primarily concerning latency, bandwidth, and cost. This paper addresses these challenges by exploring the paradigm of edge computing, which shifts computation closer to the source of data. We conduct a systematic review of existing literature to synthesize a comprehensive framework for real-time edge analytics in IoT networks. The proposed framework integrates strategies for lightweight data processing, resource management, and security to optimize decision-making across various smart city applications. Our analysis reveals that an edge-centric approach can significantly reduce latency and conserve network bandwidth, thereby enabling time-sensitive applications such as intelligent traffic management, autonomous vehicle coordination, and real-time environmental monitoring. Furthermore, we conduct an in-depth investigation into the critical challenges inherent in this paradigm, including the severe resource constraints on edge devices, complex security and privacy vulnerabilities, persistent interoperability issues, and the socio-ethical implications of pervasive urban sensing. The discussion synthesizes these findings, highlighting the profound implications for urban planners, technologists, and researchers. We conclude that the strategic implementation of real-time edge analytics is not merely a technical upgrade but a foundational enabler for creating truly responsive, efficient, and intelligent urban ecosystems that are secure, ethical, and sustainable.
Keywords
References
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