Internet Activity Forecasting Over 5G Billing Data Using Deep Learning Techniques

Tiwari, Vaibhav; Pandey, Chandrasen; Roy, Diptendu Sinha

doi:10.1109/iciccsp53532.2022.9862437

Cited by 11 publications

(2 citation statements)

References 9 publications

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“…While dense placement of ESs offers comprehensive coverage and can help Mobile Network Operators (MNOs) meet Service Level Agreements (SLAs) through reduced access times and balanced workloads, it can escalate infrastructure and energy costs [17]. Conversely, using fewer ESs might reduce costs and energy use but could avoid failing to meet SLAs and result in uneven workloads, [18]. Therefore, it is crucial to find a balance in the number of ESs deployed to optimize both cost and coverage effectively.…”

Section: Related Workmentioning

confidence: 99%

Maximizing Coverage and Energy Conservation in B5G Networks Using Hexagonal Tiling to Deploy FT-S2ES

Tiwari,

Pandey,

Singh Yadav

et al. 2024

IEEE Open J. Commun. Soc.

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Edge Servers (ESs) enhance the efficiency and reliability of modern Beyond Fifth-Generation (B5G) network systems by reducing latency, boosting responsiveness, and optimizing network usage. From an industry standpoint, the optimal positioning of ESs is critical to enhancing coverage reach while reducing potential conflicts with neighboring servers, guaranteeing consistent and reliable user access. Traditional circular coverage for ESs is suboptimal due to inherent constraints. The hexagonal model, known for symmetrical and consistent distribution, emerges as a superior choice. Implementing Fault Tolerance Edge Server Placement (FT-ESP) demands workload distribution across servers to prevent overloading and mitigate service failures. The complexity of assigning multiple Base Stations (BSs) to ESs categorizes FT-ESP as a Nondeterministic Polynomial Time (NP-hard) problem. To address this, the Subset Sum Encoding Scheme (S2ES) has been introduced, linking BSs to ESs. This research Fault Tolerance Subset Sum Encoding Scheme (FT-S2ES) examines various Edge Server Placement Problem (ESPP) strategies, highlighting the proposed method's efficacy compared to traditional techniques. The empirical findings indicate that, when tested on real-world datasets, the suggested strategy successfully achieves a balanced workload and utilization rate of 8.82% and 7.67%, respectively. Compared to the Grey Wolf Optimizer (GWO) algorithm, energy usage is significantly reduced by 32.23%. In addition, the proposed approach ensures a reduction in latency by 22.80% with an increasing number of BSs and 9.47% with an increasing number of ESs.

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Section: Related Workmentioning

confidence: 99%

Maximizing Coverage and Energy Conservation in B5G Networks Using Hexagonal Tiling to Deploy FT-S2ES

Tiwari,

Pandey,

Singh Yadav

et al. 2024

IEEE Open J. Commun. Soc.

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“…Against this backdrop, we propose a reinforcement learning based synthetic data generator tailored for MEC performance evaluation over 5G networks. The proposed generator incorporates advanced modeling techniques such as multi-head attention and bidirectional LSTM [26], [27]. These techniques enable the capture of complex spatiotemporal patterns in mobile traffic and user behavior.…”

Section: Related Workmentioning

confidence: 99%

Resource-Efficient Synthetic Data Generation for Performance Evaluation in Mobile Edge Computing Over 5G Networks

Pandey,

Tiwari,

Rathore

et al. 2023

IEEE Open J. Commun. Soc.

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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.

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Scenario-Based Visualization for Traffic Congestion Mitigation

Dkhar,

Mohanty,

Moulik

et al. 2024

Innovations in Sustainable Technologies and Computing

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Internet Activity Forecasting Over 5G Billing Data Using Deep Learning Techniques

Cited by 11 publications

References 9 publications

Maximizing Coverage and Energy Conservation in B5G Networks Using Hexagonal Tiling to Deploy FT-S2ES

Maximizing Coverage and Energy Conservation in B5G Networks Using Hexagonal Tiling to Deploy FT-S2ES

Resource-Efficient Synthetic Data Generation for Performance Evaluation in Mobile Edge Computing Over 5G Networks

Scenario-Based Visualization for Traffic Congestion Mitigation

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