Leveraging Personalized Customer Experiences in Mobile Edge Computing Through Split Learning Using Smart Data-Driven Modeling

IF 3.4 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Access Pub Date : 2025-02-19 DOI:10.1109/ACCESS.2025.3543577

V. Vinoth Kumar;K. M. Karthick Raghunath;Iyappan Perumal;K. Manikandan

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引用次数: 0

Abstract

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.

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来源期刊

IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

9.80

自引率

7.70%

发文量

6673

审稿时长

6 weeks

期刊介绍： IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest. IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on: Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals. Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering. Development of new or improved fabrication or manufacturing techniques. Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.