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Deep learning based adaptive Ryu controller model for quality of experience issues in multimedia streaming for software defined vehicular networks

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Abstract

Vehicular Ad Hoc Networks (VANETs) are becoming important with the advancement of connected technologies, yet they grapple with the pivotal challenge of ensuring quality of service (QoS) and quality of experience (QoE). This stems from the inherently erratic nature and huge data volumes. While reliability and efficiency remain paramount, the need to address QoS and QoE emerges as a critical motivation. Most current algorithms that address streaming data fall short in terms of QoS performance metrics. Thus this work strives to improve upon QoS metrics and further improve QoE. Our proposed method uses deep learning to address these problems in a realistic software-defined vehicular network (SDVN) based on the QoS and QoE. Our research aims to combine SDVN with a recurrent neural network (RNN) in Ryu SDVN controller. The RNN model encompasses a sophisticated architecture comprising multiple layers of recurrent units designed to capture temporal dependencies in data. Through a meticulously crafted training methodology utilizing techniques such as backpropagation through time, it learns to predict future network states based on historical data. Fine-tuning hyperparameters such as the number of epochs and the batch size enables optimal model convergence. We examine this method in a realistic simulation and compare its effectiveness with conventional approaches. The results show significant gains, i.e., marginal to 28% better than the nearest rival and far better than VANET. We also evaluate the network’s resilience by varying transmission rate and packet size. Our method functions well in high-density situations, suggesting that real-world deployments can benefit from it.

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Data availability

The dataset used for this study is publicly available for research purposes in the GEANT repository (https://www.geant.org/).

Abbreviations

3SSIM:

Three Component Structural Similarity Index Measurement

App:

Application

AR:

Augmented Reality

BPTT:

Backpropagation Through Time

cont:

Contrast

DDoS:

Distributed Denial of Service

DDPG:

Deep Deterministic Policy Gradient

DRL:

Deep Reinforcement Learning

EED:

End-to-End Delay

GRU:

Gate Recurrent Unit

IoV:

Internet of Vehicles

LSTM:

Long Short Term Memory

lumi:

Luminance

Mbps:

Megabits per second

MBS:

Macro Base Stations

mBSs:

mmWave base stations

MDP:

Markov Decision Process

MSE:

Mean Squared Error

MSAD:

Mean Sum of Absolute Difference

msec:

millisecond

MSSIM:

Multiscale Structural Similarity Index Measurement

NE:

Noise Estimation

PDR:

Packet Delivery Ratio

PSNR:

Peak Signal-to-Noise Ratio

REST:

Representational State Transfer

RL:

Reinforcement Learning

RNN:

Recurrent Neural Network

QoE:

Quality of Experience

QoS:

Quality of Service

SDN:

Software Defined Network

SDVN:

Software-Defined Vehicular Network

SSAE:

Stacked Sparse AutoEncoders

SSIM:

Structural Similarity Index Measure

stru:

Structure

SUMO:

Simulation of Urban Mobility

TP:

Throughput

VANET:

Vehicular Ad Hoc Network

VR:

Virtual Reality

VQM:

Video Quality Metric

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Author notes

  1. Varun P. Sarvade and Shrirang Ambaji Kulkarni contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science and Engineering, Research Center The National Institute of Engineering, Visvesvaraya Technological University, Belagavi, 590018, India

    Varun P. Sarvade

  2. Department of Computer Science and Engineering, Research Center The National Institute of Engineering, Visvesvaraya Technological University, Belagavi, 590018, India

    Shrirang Ambaji Kulkarni

  3. Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India

    Shrirang Ambaji Kulkarni

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  1. Varun P. Sarvade
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Correspondence to Shrirang Ambaji Kulkarni.

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Sarvade, V.P., Kulkarni, S.A. Deep learning based adaptive Ryu controller model for quality of experience issues in multimedia streaming for software defined vehicular networks. Appl Intell 54, 9543–9564 (2024). https://doi.org/10.1007/s10489-024-05642-4

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