Application-Based Online Traffic Classification with Deep Learning Models on SDN Networks

Chang, Lin-Huang; Lee, Tsung‐Han; Chu, Hung‐Chi; Su, Chauchin

doi:10.46604/aiti.2020.4286

Cited by 22 publications

(18 citation statements)

References 17 publications

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“…The emergence of advanced location-aware technology has led to the generation of various mobility data. The massive amount of movement data could completely reshape our understanding of urban planning, transport management, and human mobility, because movement analytics enables us to reveal valuable and hidden patterns from big spatiotemporal data [1][2][3][4][5]. In particular, accurate traffic forecasting, including traffic flow and speed, is an essential factor for smart cities and transportation management [6].…”

Section: Introductionmentioning

confidence: 99%

Highway Speed Prediction Using Gated Recurrent Unit Neural Networks

et al. 2021

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Movement analytics and mobility insights play a crucial role in urban planning and transportation management. The plethora of mobility data sources, such as GPS trajectories, poses new challenges and opportunities for understanding and predicting movement patterns. In this study, we predict highway speed using a gated recurrent unit (GRU) neural network. Based on statistical models, previous approaches suffer from the inherited features of traffic data, such as nonlinear problems. The proposed method predicts highway speed based on the GRU method after training on digital tachograph data (DTG). The DTG data were recorded in one month, giving approximately 300 million records. These data included the velocity and locations of vehicles on the highway. Experimental results demonstrate that the GRU-based deep learning approach outperformed the state-of-the-art alternatives, the autoregressive integrated moving average model, and the long short-term neural network (LSTM) model, in terms of prediction accuracy. Further, the computational cost of the GRU model was lower than that of the LSTM. The proposed method can be applied to traffic prediction and intelligent transportation systems.

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Section: Introductionmentioning

confidence: 99%

Highway Speed Prediction Using Gated Recurrent Unit Neural Networks

et al. 2021

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“…Chang et al [97] proposed an application for offline and online traffic classification. More specifically, the authors used three DL-based models, including CNN, MLP, and SAE models.…”

Section: Fine-grained Traffic Classificationmentioning

confidence: 99%

Intelligent Traffic Management in Next-Generation Networks

2022

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The recent development of smart devices has lead to an explosion in data generation and heterogeneity. Hence, current networks should evolve to become more intelligent, efficient, and most importantly, scalable in order to deal with the evolution of network traffic. In recent years, network softwarization has drawn significant attention from both industry and academia, as it is essential for the flexible control of networks. At the same time, machine learning (ML) and especially deep learning (DL) methods have also been deployed to solve complex problems without explicit programming. These methods can model and learn network traffic behavior using training data/environments. The research community has advocated the application of ML/DL in softwarized environments for network traffic management, including traffic classification, prediction, and anomaly detection. In this paper, we survey the state of the art on these topics. We start by presenting a comprehensive background beginning from conventional ML algorithms and DL and follow this with a focus on different dimensionality reduction techniques. Afterward, we present the study of ML/DL applications in sofwarized environments. Finally, we highlight the issues and challenges that should be considered.

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“…Parallel and distributed simulation refers to technologies that enable a simulation model to execute on multiple processors [4]. Its benefit includes reduced execution time, larger model scale, and integration with other simulators [5][6][7]. Parallel and distributed simulations can be distinguished by the geographical distribution, the composition of the processors used, and the network to interconnect the processors.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Under the synchronization algorithms, LPs execute simulations while obeying a rule known as Local Causality Constraint (LCC). Two different synchronization approaches have been proposed to satisfy the local causality constraint, conservative execution, and optimistic execution [4][5][6][7][8][9][10][11][12]. LPs in conservative synchronization protocols strictly avoid violating LCC.…”

Section: Literature Reviewmentioning

confidence: 99%

Graphical Analysis of Rollback Process in Ad Hoc Distributed Traffic Simulation

Suh

2020

Applied Sciences

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Recent advancements in sensor, mobile computing, and wireless communication technologies is creating new opportunities to effectively exploit real-time traffic data. Onboard vehicles collect, process, simulate traffic states in a distributed fashion and a local transportation management center coordinates the overall simulation with an optimistic execution technique. Such a distributed approach can provide more up-to-date and robust estimates with decreased communication bandwidth requirements and increased computing capacity. This paper proposes an online ad hoc distributed simulation. The physical operating platform for the model including operating system, communicational middleware, and traffic simulation model are described. Also, this paper investigates the analytical background of rollback process of the proposed ad hoc distributed simulation model. Flow rate diagram and cumulative number of vehicle diagram show that the overall system simulation speed and estimate accuracy may differ significantly as a function of the selected threshold.

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Application-Based Online Traffic Classification with Deep Learning Models on SDN Networks

Cited by 22 publications

References 17 publications

Highway Speed Prediction Using Gated Recurrent Unit Neural Networks

Highway Speed Prediction Using Gated Recurrent Unit Neural Networks

Intelligent Traffic Management in Next-Generation Networks

Graphical Analysis of Rollback Process in Ad Hoc Distributed Traffic Simulation

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