Leveraging Personalized Customer Experiences in Mobile Edge Computing Through Split Learning Using Smart Data-Driven Modeling
The rapid growth and opportunities of Mobile Edge Computing (MEC) technology have transformed smart consumer electronics like smartphones and wearable devices that produce massive and complicated data sets. Analyzing consumer electronics data to understand customers is difficult because of its vast...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10892096/ |
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| author | V. Vinoth Kumar K. M. Karthick Raghunath Iyappan Perumal K. Manikandan |
| author_facet | V. Vinoth Kumar K. M. Karthick Raghunath Iyappan Perumal K. Manikandan |
| author_sort | V. Vinoth Kumar |
| collection | DOAJ |
| description | The rapid growth and opportunities of Mobile Edge Computing (MEC) technology have transformed smart consumer electronics like smartphones and wearable devices that produce massive and complicated data sets. Analyzing consumer electronics data to understand customers is difficult because of its vast scale and detailed content. To solve these issues, we present Enhanced Smart Data-Driven Modeling (ESDDM), which combines Smart Data-Driven Modeling (SDDM) with modern Deep Learning (DL). ESDDM combines multiple data streams to better understand consumer electronics systems beyond the normal Machine Learning (ML) capabilities. ESDDM’s integration with Split Learning (SL) protects data by lessening transmission risks and avoiding central cloud storage while keeping sensitive information secure on user devices and delivering better system performance. The prediction results from ESDDM show its strength with a low Mean Squared Error (MSE) of 0.267, which reveals its capability to reshape the MEC domain while creating business possibilities and better customer outcomes. |
| format | Article |
| id | doaj-art-a11bdfd3a66e4e8a9d95183944674802 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a11bdfd3a66e4e8a9d951839446748022025-08-20T03:44:02ZengIEEEIEEE Access2169-35362025-01-0113344353444810.1109/ACCESS.2025.354357710892096Leveraging Personalized Customer Experiences in Mobile Edge Computing Through Split Learning Using Smart Data-Driven ModelingV. Vinoth Kumar0https://orcid.org/0000-0003-1070-3212K. M. Karthick Raghunath1Iyappan Perumal2https://orcid.org/0000-0003-3104-8149K. Manikandan3School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaDepartment of Computer Science and Engineering, JAIN (Deemed-to-be University), Bengaluru, Karnataka, IndiaSchool of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaSchool of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaThe rapid growth and opportunities of Mobile Edge Computing (MEC) technology have transformed smart consumer electronics like smartphones and wearable devices that produce massive and complicated data sets. Analyzing consumer electronics data to understand customers is difficult because of its vast scale and detailed content. To solve these issues, we present Enhanced Smart Data-Driven Modeling (ESDDM), which combines Smart Data-Driven Modeling (SDDM) with modern Deep Learning (DL). ESDDM combines multiple data streams to better understand consumer electronics systems beyond the normal Machine Learning (ML) capabilities. ESDDM’s integration with Split Learning (SL) protects data by lessening transmission risks and avoiding central cloud storage while keeping sensitive information secure on user devices and delivering better system performance. The prediction results from ESDDM show its strength with a low Mean Squared Error (MSE) of 0.267, which reveals its capability to reshape the MEC domain while creating business possibilities and better customer outcomes.https://ieeexplore.ieee.org/document/10892096/Contextual adaptationfeature engineeringmobile edge computingpredictive analyticssplit learning |
| spellingShingle | V. Vinoth Kumar K. M. Karthick Raghunath Iyappan Perumal K. Manikandan Leveraging Personalized Customer Experiences in Mobile Edge Computing Through Split Learning Using Smart Data-Driven Modeling IEEE Access Contextual adaptation feature engineering mobile edge computing predictive analytics split learning |
| title | Leveraging Personalized Customer Experiences in Mobile Edge Computing Through Split Learning Using Smart Data-Driven Modeling |
| title_full | Leveraging Personalized Customer Experiences in Mobile Edge Computing Through Split Learning Using Smart Data-Driven Modeling |
| title_fullStr | Leveraging Personalized Customer Experiences in Mobile Edge Computing Through Split Learning Using Smart Data-Driven Modeling |
| title_full_unstemmed | Leveraging Personalized Customer Experiences in Mobile Edge Computing Through Split Learning Using Smart Data-Driven Modeling |
| title_short | Leveraging Personalized Customer Experiences in Mobile Edge Computing Through Split Learning Using Smart Data-Driven Modeling |
| title_sort | leveraging personalized customer experiences in mobile edge computing through split learning using smart data driven modeling |
| topic | Contextual adaptation feature engineering mobile edge computing predictive analytics split learning |
| url | https://ieeexplore.ieee.org/document/10892096/ |
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