Resource-Efficient Synthetic Data Generation for Performance Evaluation in Mobile Edge Computing Over 5G Networks
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-effic...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Open Journal of the Communications Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10221869/ |
| _version_ | 1797690253601931264 |
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| author | Chandrasen Pandey Vaibhav Tiwari Rajkumar Singh Rathore Rutvij H. Jhaveri Diptendu Sinha Roy Shitharth Selvarajan |
| author_facet | Chandrasen Pandey Vaibhav Tiwari Rajkumar Singh Rathore Rutvij H. Jhaveri Diptendu Sinha Roy Shitharth Selvarajan |
| author_sort | Chandrasen Pandey |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T01:56:54Z |
| format | Article |
| id | doaj.art-23530ba7cf82442ebc2a255f2166570a |
| institution | Directory Open Access Journal |
| issn | 2644-125X |
| language | English |
| last_indexed | 2024-03-12T01:56:54Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj.art-23530ba7cf82442ebc2a255f2166570a2023-09-07T23:00:52ZengIEEEIEEE Open Journal of the Communications Society2644-125X2023-01-0141866187810.1109/OJCOMS.2023.330603910221869Resource-Efficient Synthetic Data Generation for Performance Evaluation in Mobile Edge Computing Over 5G NetworksChandrasen Pandey0https://orcid.org/0000-0002-7031-1619Vaibhav Tiwari1https://orcid.org/0000-0003-0205-0514Rajkumar Singh Rathore2https://orcid.org/0000-0003-4571-1888Rutvij H. Jhaveri3https://orcid.org/0000-0002-3285-7346Diptendu Sinha Roy4https://orcid.org/0000-0001-9731-2534Shitharth Selvarajan5https://orcid.org/0000-0002-4931-724XDepartment of Computer Science and Engineering, National Institute of Technology Meghalaya, Shillong, IndiaDepartment of Computer Science and Engineering, National Institute of Technology Meghalaya, Shillong, IndiaDepartment of Computer Science, Cardiff School of Technologies, Cardiff Metropolitan University, Llandaff Campus, Cardiff, U.K.Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, IndiaDepartment of Computer Science and Engineering, National Institute of Technology Meghalaya, Shillong, IndiaDepartment of Computer Science, Kebri Dehar University, Kebri Dehar, EthiopiaMobile 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.https://ieeexplore.ieee.org/document/10221869/Generative adversarial network5Gmobile edge computingsynthetic data generationresource efficiencyperformance evaluation |
| spellingShingle | Chandrasen Pandey Vaibhav Tiwari Rajkumar Singh Rathore Rutvij H. Jhaveri Diptendu Sinha Roy Shitharth Selvarajan Resource-Efficient Synthetic Data Generation for Performance Evaluation in Mobile Edge Computing Over 5G Networks IEEE Open Journal of the Communications Society Generative adversarial network 5G mobile edge computing synthetic data generation resource efficiency performance evaluation |
| title | Resource-Efficient Synthetic Data Generation for Performance Evaluation in Mobile Edge Computing Over 5G Networks |
| title_full | Resource-Efficient Synthetic Data Generation for Performance Evaluation in Mobile Edge Computing Over 5G Networks |
| title_fullStr | Resource-Efficient Synthetic Data Generation for Performance Evaluation in Mobile Edge Computing Over 5G Networks |
| title_full_unstemmed | Resource-Efficient Synthetic Data Generation for Performance Evaluation in Mobile Edge Computing Over 5G Networks |
| title_short | Resource-Efficient Synthetic Data Generation for Performance Evaluation in Mobile Edge Computing Over 5G Networks |
| title_sort | resource efficient synthetic data generation for performance evaluation in mobile edge computing over 5g networks |
| topic | Generative adversarial network 5G mobile edge computing synthetic data generation resource efficiency performance evaluation |
| url | https://ieeexplore.ieee.org/document/10221869/ |
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