An edge intelligence-enhanced quantitative assessment model for implicit working gain under mobile internet of things
Edge intelligence refers to a novel operation mode in which intelligent algorithms are implemented in edge devices to break the limitation of computing power. In the context of big data, mobile computing has been an effective assistive tool in many cross-field areas, in which quantitative assessment...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2023-02-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023326?viewType=HTML |
| _version_ | 1811159712977125376 |
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| author | Xiangshuai Duan Naiping Song Fu Mo |
| author_facet | Xiangshuai Duan Naiping Song Fu Mo |
| author_sort | Xiangshuai Duan |
| collection | DOAJ |
| description | Edge intelligence refers to a novel operation mode in which intelligent algorithms are implemented in edge devices to break the limitation of computing power. In the context of big data, mobile computing has been an effective assistive tool in many cross-field areas, in which quantitative assessment of implicit working gain is typical. Relying on the strong ability of data integration provided by the Internet of Things (IoT), intelligent algorithms can be equipped into terminals to realize intelligent data analysis. This work takes the assessment of working gain in universities as the main problem scenario, an edge intelligence-enhanced quantitative assessment model for implicit working gain under mobile IoT. Based on fundamental data acquisition from deployed mobile IoT environment, all the distributed edge terminals are employed to implement machine learning algorithms to formulate a quantitative assessment model. The dataset collected from a real-world application is utilized to evaluate the performance of the proposed mobile edge computing framework, and proper performance can be obtained and observed. |
| first_indexed | 2024-04-10T05:46:57Z |
| format | Article |
| id | doaj.art-e9263025db9d4f68a8eb1b8e38b3d984 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-04-10T05:46:57Z |
| publishDate | 2023-02-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-e9263025db9d4f68a8eb1b8e38b3d9842023-03-06T01:15:45ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-02-012047548756410.3934/mbe.2023326An edge intelligence-enhanced quantitative assessment model for implicit working gain under mobile internet of thingsXiangshuai Duan0Naiping Song1Fu Mo21. President's Office, Jiangsu University of Technology, Changzhou 213001, China2. School of Computer Engineering, Jiangsu University of Technology, Changzhou 213001, China3. School of Mechanical and Electrical Engineering, Guangdong University of Science and Technology, Dongguan 523083, ChinaEdge intelligence refers to a novel operation mode in which intelligent algorithms are implemented in edge devices to break the limitation of computing power. In the context of big data, mobile computing has been an effective assistive tool in many cross-field areas, in which quantitative assessment of implicit working gain is typical. Relying on the strong ability of data integration provided by the Internet of Things (IoT), intelligent algorithms can be equipped into terminals to realize intelligent data analysis. This work takes the assessment of working gain in universities as the main problem scenario, an edge intelligence-enhanced quantitative assessment model for implicit working gain under mobile IoT. Based on fundamental data acquisition from deployed mobile IoT environment, all the distributed edge terminals are employed to implement machine learning algorithms to formulate a quantitative assessment model. The dataset collected from a real-world application is utilized to evaluate the performance of the proposed mobile edge computing framework, and proper performance can be obtained and observed.https://www.aimspress.com/article/doi/10.3934/mbe.2023326?viewType=HTMLedge intelligencemobile iotquantitative assessment modelmobile computingmachine learning |
| spellingShingle | Xiangshuai Duan Naiping Song Fu Mo An edge intelligence-enhanced quantitative assessment model for implicit working gain under mobile internet of things Mathematical Biosciences and Engineering edge intelligence mobile iot quantitative assessment model mobile computing machine learning |
| title | An edge intelligence-enhanced quantitative assessment model for implicit working gain under mobile internet of things |
| title_full | An edge intelligence-enhanced quantitative assessment model for implicit working gain under mobile internet of things |
| title_fullStr | An edge intelligence-enhanced quantitative assessment model for implicit working gain under mobile internet of things |
| title_full_unstemmed | An edge intelligence-enhanced quantitative assessment model for implicit working gain under mobile internet of things |
| title_short | An edge intelligence-enhanced quantitative assessment model for implicit working gain under mobile internet of things |
| title_sort | edge intelligence enhanced quantitative assessment model for implicit working gain under mobile internet of things |
| topic | edge intelligence mobile iot quantitative assessment model mobile computing machine learning |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023326?viewType=HTML |
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