A Centralized and Dynamic Network Congestion Classification Approach for Heterogeneous Vehicular Networks

Falahatraftar, Farnoush; Pierre, Samuel; Chamberland, Steven

doi:10.1109/access.2021.3108425

Cited by 12 publications

(5 citation statements)

References 43 publications

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“…Moreover, researchers have explored predictive methods for identifying congestion warning conditions. For instance, in [23], a Naive Bayesian algorithm was employed to forecast congestion in HetVNET data transmission, utilizing features such as car density, data rate, and transmission powers. Similarly, in [24], scheduling techniques were proposed within download managers to monitor network conditions and facilitate large file transfers during off-peak times.…”

Section: A Congestion Detectionmentioning

confidence: 99%

Optimizing the Over and Underutilization of Network Resources During Peak and Off-Peak Hours

Badshah,

Daud,

Khan

et al. 2024

IEEE Access

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In 2022, the digital population reached 5.4 billion, leading to network congestion, primarily attributed to the widespread use of social media, robotic programs, and prime-time television during peak hours. This congestion posed a significant challenge, as sensitive services struggled to operate effectively during peak hours, while network resources remained largely underutilized during off-peak hours. To tackle this issue, we propose the implementation of Regional Servers. These servers are designed to initially store less critical data during peak hours and subsequently transfer it to the cloud during off-peak hours, effectively optimizing network resources. The results demonstrate that the deployment of regional servers proved highly efficient in mitigating delays and reducing jitters that users typically experience during peak hours, successfully addressing the challenges posed by network congestion.

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Section: A Congestion Detectionmentioning

confidence: 99%

Optimizing the Over and Underutilization of Network Resources During Peak and Off-Peak Hours

Badshah,

Daud,

Khan

et al. 2024

IEEE Access

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“…ML and DL, as a subset of AI, can be utilized for developing effective models and provide a better and higher rate of prediction accuracy [6], [14]. Although ML approaches provide better performance than the traditional model, each of them has challenges and issues.…”

Section: Background and Related Workmentioning

confidence: 99%

An Ensemble-Based Machine Learning Model for Forecasting Network Traffic in VANET

Amiri¹,

Pierre²

2023

IEEE Access

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Vehicular Ad-hoc Networks (VANETs), as the most significant element of the Intelligent Transportation Systems (ITS), have the potential to enhance traffic efficiency and road safety by making the transportation system smarter and are still at the initial point of development. In this paper, we propose an ensemble-based machine learning model for network traffic prediction in VANET. We take advantage of Ensemble Learning (EL), which combines different Machine Learning (ML) models to achieve better performance and improve accuracy. We consider the most informative attributes of the VANET dataset using Boruta and LightGBM as ensemble feature selection methods. Our proposed model is based on Stacking Ensemble Learning with Booster Model (STK-EBM) designed with a stacking ensemble of heterogeneous ML models. The framework of the proposed model consists of two layers, including a base layer and a meta layer. The first layer integrates Random Forest (RF), K-Nearest Neighbor (KNN) and XGBoost as a booster of the base learners. An optimized Logistic Regression (LR) employs as our meta learner in the second layer. We evaluate the performance of our model considering classification metrics and then compare it with the most popular traffic predictive models. Simulation results show that the STK-EBM model gives a more stable prediction than the single algorithm, as well as better overall performance in terms of prediction accuracy and execution time.

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“…However, a variety of useful parameters can be considered by those intelligent techniques for anticipating the traffic flow. The use of ML and DL techniques can also be involved in analyzing data and produce more accurate predictions [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Design and Implementation of Intelligent Traffic-Management System for Smart Cities using Roaming Agent and Deep Neural Network (RAD2N)

Sankar,

Joel,

Husain

2023

IJRITCC

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In metropolitan areas, the exponential growth in quantity of vehicles has instigated gridlock, pollution, and delays in the transportation of freight. IoT is the modern revolution which pushes the world towards intelligent management systems and automated procedures. This makes a significant contribution to automation and intelligent societies. Traffic regulation and effective congestion management assist conserve many priceless resources. In order to recognize, collect and send data, autonomous vehicles are furnished with IoT powered Intelligent Traffic Management System (ITMS) having a set of sensors. Moreover, machine learning (ML) algorithms can also be employed to enhance the transportation system. Traffic jams, delays, and a high death rate are the results of the problems that the current transport management systems face. In this paper, an active traffic control for VANET is proposed which merges Roaming Agents (RA) with deep neural networks (DNN). The effectiveness of the DNN with RA (RAD2N) routing method in VANETs is evaluated experimentally and compared with the traditional ML and other DL routing algorithms. Several traffic congestion indicators, including delay, packet delivery ratio (PDR) and throughput are used to validate RAD2N. The outcomes demonstrate that the proposed approach delivers lower latency and energy consumption.

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A Centralized and Dynamic Network Congestion Classification Approach for Heterogeneous Vehicular Networks

Cited by 12 publications

References 43 publications

Optimizing the Over and Underutilization of Network Resources During Peak and Off-Peak Hours

Optimizing the Over and Underutilization of Network Resources During Peak and Off-Peak Hours

An Ensemble-Based Machine Learning Model for Forecasting Network Traffic in VANET

Design and Implementation of Intelligent Traffic-Management System for Smart Cities using Roaming Agent and Deep Neural Network (RAD2N)

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