An Empirical Study on AI-Powered Edge Computing Architectures for Real-Time IoT Applications

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dc.contributor.authorNgu, Anne H. H.
dc.contributor.authorYasmin, Awatif
dc.date.accessioned2024-04-11T18:57:21Z
dc.date.available2024-04-11T18:57:21Z
dc.date.issued2024-03
dc.description.abstractEdge computing is indispensable for IoT applications, handling data from billions of devices and expected to surpass 41.6 billion installations by 2023. It facilitates swift decision-making at the device level. It conserves network bandwidth by processing data locally, making it suitable for resource-constrained or costly networks. Bolsters privacy and security by storing data locally, particularly crucial for applications that involves processing personal data.
dc.description.departmentComputer Science
dc.formatImage
dc.format.extent1 page
dc.format.medium1 file (.pdf)
dc.identifier.citationNgu, A. H. H., & Yasmin, A. (2024). An empirical study on AI-powered edge computing architectures for real-time IoT applications. Poster presented at the Health Scholar Showcase, Translational Health Research Center, San Marcos, Texas.
dc.identifier.urihttps://hdl.handle.net/10877/18440
dc.language.isoen
dc.sourceHealth Scholar Showcase, 2024, Texas State University Translational Health Science Center, San Marcos, Texas, United States.
dc.subjectAI
dc.subjectcomputing architectures
dc.subjectIoT applications
dc.titleAn Empirical Study on AI-Powered Edge Computing Architectures for Real-Time IoT Applications
dc.typePoster

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