Resource-Efficient Synthetic Data Generation for Performance Evaluation in Mobile Edge Computing Over 5G Networks Article Swipe
Chandrasen Pandey
,
Vaibhav Tiwari
,
Rajkumar Singh Rathore
,
Rutvij H. Jhaveri
,
Diptendu Sinha Roy
,
Shitharth Selvarajan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/ojcoms.2023.3306039
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/ojcoms.2023.3306039
Mobile Edge Computing (MEC) in 5G networks has emerged as a promising technology to enable efficient and low-latency services for mobile users. In this paper, we present a novel synthetic data generation approach tailored for evaluating MEC in 5G networks. Our methodology incorporates resource-efficient techniques to generate realistic synthetic datasets that capture the spatio-temporal patterns of mobile traffic and user behavior. By leveraging advanced modeling techniques, including multi-head attention and bidirectional LSTM, we accurately model the complex dependencies in the data while optimizing computational resources. The proposed synthetic data generator enables the creation of diverse datasets that closely resemble real-world scenarios, facilitating the evaluation of MEC performance and optimizing resource utilization. Through extensive experiments and evaluations, we demonstrate the effectiveness of our approach in enabling accurate assessments of MEC in 5G networks. Our work contributes to the field by providing a robust methodology for synthetic data generation specifically tailored for MEC evaluation, addressing the need for resource-efficient evaluation frameworks in the context of emerging technologies. The results of our study provide valuable insights for the design and optimization of MEC systems in real-world deployments.
Related Topics
Concepts
Computer science
Mobile edge computing
Synthetic data
Distributed computing
Big data
Edge computing
Field (mathematics)
Resource (disambiguation)
Enhanced Data Rates for GSM Evolution
Context (archaeology)
Next-generation network
Data science
Data mining
Artificial intelligence
Computer network
Mathematics
Pure mathematics
Paleontology
The Internet
Biology
World Wide Web
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/ojcoms.2023.3306039
- https://ieeexplore.ieee.org/ielx7/8782661/8901158/10221869.pdf
- OA Status
- gold
- Cited By
- 12
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385945334
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4385945334Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/ojcoms.2023.3306039Digital Object Identifier
- Title
-
Resource-Efficient Synthetic Data Generation for Performance Evaluation in Mobile Edge Computing Over 5G NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Chandrasen Pandey, Vaibhav Tiwari, Rajkumar Singh Rathore, Rutvij H. Jhaveri, Diptendu Sinha Roy, Shitharth SelvarajanList of authors in order
- Landing page
-
https://doi.org/10.1109/ojcoms.2023.3306039Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/8782661/8901158/10221869.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/8782661/8901158/10221869.pdfDirect OA link when available
- Concepts
-
Computer science, Mobile edge computing, Synthetic data, Distributed computing, Big data, Edge computing, Field (mathematics), Resource (disambiguation), Enhanced Data Rates for GSM Evolution, Context (archaeology), Next-generation network, Data science, Data mining, Artificial intelligence, Computer network, Mathematics, Pure mathematics, Paleontology, The Internet, Biology, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 8, 2024: 3, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
31Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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