Henry Gas Solubility Optimization with Deep Learning Enabled Traffic Flow Forecasting in 6G Enabled Vehicular Networks

Escorcia‐Gutierrez, José; Torres‐Torres, Melitsa; Madera, Natasha; Soto, Carlos Y.

doi:10.1002/9781119812722.ch3

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Escorcia et al [158] proposed HGSODL-TFF, a novel traffic flow forecasting technique tailored for 6G-enabled vehicular networks in Vehicular Ad Hoc Networks (VANETs). The approach integrates the HGSO algorithm with a Deep Belief Network (DBN) model, optimizing hyperparameters like learning rate and batch size.…”

Section: B Prediction and Classificationmentioning

confidence: 99%

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

El-Shorbagy,

Bouaouda,

Nabwey

et al. 2024

IEEE Access

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The Henry Gas Solubility Optimization (HGSO) is a physics-based metaheuristic inspired by Henry's law, which describes the solubility of the gas in a liquid under specific pressure conditions. Since its introduction by Hashim et al. in 2019, HGSO has gained significant attention for its unique features, including minimal adaptive parameters and a balanced exploration-exploitation trade-off, leading to favorable convergence. This study provides an up-to-date survey of HGSO, covering the walk through the historical development of HGSO, its modifications, and hybridizations with other algorithms, showcasing its adaptability and potential for synergy. Recent variants of HGSO are categorized into modified, hybridized, and multi-objective versions, and the review explores its main applications, demonstrating its effectiveness in solving complex problems. The evaluation includes a discussion of the algorithm's strengths and weaknesses. This comprehensive review, featuring graphical and tabular comparisons, not only indicates potential future directions in the field but also serves as a valuable resource for researchers seeking a deep understanding of HGSO and its advanced versions. As physics-based metaheuristic algorithms gain prominence for solving intricate optimization problems, this study provides insights into the adaptability and applications of HGSO across diverse domains.

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Section: B Prediction and Classificationmentioning

confidence: 99%

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

El-Shorbagy,

Bouaouda,

Nabwey

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The objective of the research study in [36] was to optimize the gas solubility and forecast the traffic flow in vehicular networks enabled by 6G. The authors emphasize that gas solubility is an essential aspect of vehicular networks as it impacts the energy consumption, quality of service, and safety of vehicles.…”

Section: Related Workmentioning

confidence: 99%

A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network

Farhan

Khan

Rizwan

et al. 2023

Sensors

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The roadside unit (RSU) is one of the fundamental components in a vehicular ad hoc network (VANET), where a vehicle communicates in infrastructure mode. The RSU has multiple functions, including the sharing of emergency messages and the updating of vehicles about the traffic situation. Deploying and managing a static RSU (sRSU) requires considerable capital and operating expenditures (CAPEX and OPEX), leading to RSUs that are sparsely distributed, continuous handovers amongst RSUs, and, more importantly, frequent RSU interruptions. At present, researchers remain focused on multiple parameters in the sRSU to improve the vehicle-to-infrastructure (V2I) communication; however, in this research, the mobile RSU (mRSU), an emerging concept for sixth-generation (6G) edge computing vehicular ad hoc networks (VANETs), is proposed to improve the connectivity and efficiency of communication among V2I. In addition to this, the mRSU can serve as a computing resource for edge computing applications. This paper proposes a novel energy-efficient reservation technique for edge computing in 6G VANETs that provides an energy-efficient, reservation-based, cost-effective solution by introducing the concept of the mRSU. The simulation outcomes demonstrate that the mRSU exhibits superior performance compared to the sRSU in multiple aspects. The mRSU surpasses the sRSU with a packet delivery ratio improvement of 7.7%, a throughput increase of 5.1%, a reduction in end-to-end delay by 4.4%, and a decrease in hop count by 8.7%. The results are generated across diverse propagation models, employing realistic urban scenarios with varying packet sizes and numbers of vehicles. However, it is important to note that the enhanced performance parameters and improved connectivity with more nodes lead to a significant increase in energy consumption by 2%.

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Driven traffic flow prediction in smart cities using hunter-prey optimization with hybrid deep learning models

Alzughaibi,

Karim,

Darwish

2024

Alexandria Engineering Journal

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Henry Gas Solubility Optimization with Deep Learning Enabled Traffic Flow Forecasting in 6G Enabled Vehicular Networks

Cited by 3 publications

References 14 publications

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network

Driven traffic flow prediction in smart cities using hunter-prey optimization with hybrid deep learning models

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