The increasing deployment of smart city technologies has led to a surge in data generation, particularly from real-time video analytics. This paper focuses on the integration of intelligent edge computing in the context of real-time video analytics, aiming to enhance the efficiency and responsiveness of smart city applications. The abstract discusses the challenges posed by centralized data processing and proposes an intelligent edge computing framework that leverages distributed computing resources at the network edge. By processing video data closer to where it is generated, the framework reduces latency, improves bandwidth efficiency, and enhances privacy and security. The paper further explores the use of advanced machine learning algorithms to enable real-time video analysis for tasks such as traffic management, public safety, and environmental monitoring. It concludes with a discussion on the potential benefits and future directions for integrating intelligent edge computing into the fabric of smart cities.
Taylor, M. Intelligent Edge Computing for Real-Time Video Analytics in Smart Cities. Information Sciences and Technological Innovations, 2022, 4, 30. https://doi.org/10.69610/j.isti.20220615
AMA Style
Taylor M. Intelligent Edge Computing for Real-Time Video Analytics in Smart Cities. Information Sciences and Technological Innovations; 2022, 4(1):30. https://doi.org/10.69610/j.isti.20220615
Chicago/Turabian Style
Taylor, Michael 2022. "Intelligent Edge Computing for Real-Time Video Analytics in Smart Cities" Information Sciences and Technological Innovations 4, no.1:30. https://doi.org/10.69610/j.isti.20220615
APA style
Taylor, M. (2022). Intelligent Edge Computing for Real-Time Video Analytics in Smart Cities. Information Sciences and Technological Innovations, 4(1), 30. https://doi.org/10.69610/j.isti.20220615
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