Version 1
: Received: 9 October 2024 / Approved: 9 October 2024 / Online: 11 October 2024 (03:09:29 CEST)
How to cite:
Zhou, S.; Sun, J.; Xu, K. AI-Driven Data Processing and Decision Optimization in IoT through Edge Computing and Cloud Architecture. Preprints2024, 2024100736. https://doi.org/10.20944/preprints202410.0736.v1
Zhou, S.; Sun, J.; Xu, K. AI-Driven Data Processing and Decision Optimization in IoT through Edge Computing and Cloud Architecture. Preprints 2024, 2024100736. https://doi.org/10.20944/preprints202410.0736.v1
Zhou, S.; Sun, J.; Xu, K. AI-Driven Data Processing and Decision Optimization in IoT through Edge Computing and Cloud Architecture. Preprints2024, 2024100736. https://doi.org/10.20944/preprints202410.0736.v1
APA Style
Zhou, S., Sun, J., & Xu, K. (2024). AI-Driven Data Processing and Decision Optimization in IoT through Edge Computing and Cloud Architecture. Preprints. https://doi.org/10.20944/preprints202410.0736.v1
Chicago/Turabian Style
Zhou, S., Jun Sun and Kangming Xu. 2024 "AI-Driven Data Processing and Decision Optimization in IoT through Edge Computing and Cloud Architecture" Preprints. https://doi.org/10.20944/preprints202410.0736.v1
Abstract
This study discussion point of this paper is to make an in-depth analysis of the development impact of the Internet of Things combined with edge computing and artificial intelligence. In the analysis process, the importance and criticality of data processing and decision making of edge computing as well as the challenges faced should be elaborated respectively. With the rapid popularization and development of Internet of Things devices, edge computing has brought more innovative solutions for different application scenarios such as intelligent furniture industrialization, automatic driving and intelligent transportation by reducing the delay of processing data and improving the characteristics of security data, film and television. Resource and energy efficiency have certain limitations, so it is necessary to combine artificial intelligence to enhance edge computing devices, hardware accelerators, and its utility and federated learning technologies, which can effectively improve the performance and scalability of edge computing and promote the development of more self-service network systems for smart devices. The core of this study is how to promote the Internet through AI-driven edge computing to further develop and provide insights for research priorities and suggest related future research directions.
Keywords
edge computing; artificial intelligence; internet of things; data privacy; energy efficiency
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
