Deep Learning for Predicting Traffic in V2X Networks

Abdellah, Ali R.; Muthanna, Ammar; Essai, Mohamed H.; Koucheryavy, Andrey

doi:10.3390/app121910030

Cited by 10 publications

(3 citation statements)

References 29 publications

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“…As the count of connected and autonomous vehicles increases, there will be a greater demand for services such as 3-D videos, holographic display systems, immersive entertainment, and improved in-car infotainment [30]. These developments will thrust the capacity bounds of current wireless networks and pose unconventional challenges to V2X networks in terms of bandwidth, delay, signals coverage, spectral utilization, energy consumption, cost competence, AI level, virtualization, and predominately security [31].…”

Section: A V2x Communicationmentioning

confidence: 99%

Security of 6G-Enabled Vehicle-to-Everything Communication in Emerging Federated Learning and Blockchain Technologies

Kim,

Oh,

Yim

et al. 2024

IEEE Access

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Sixth-generation (6G) communication is emerging as seamless and massive connecting of almost everything. Therefore, vehicles, being extensively linked with human mobility, require a technological pace for compatibility with the 6G era. 6G will also potentially revolutionize the Vehicle-to-Everything (V2X) communication. However, this modernization will surface several security challenges in the complex heterogeneous architecture of V2X communication in 6G. Similarly, the expansion of V2X also introduces unconventional security risks and vulnerabilities. This paper aims to provide an overview of the security challenges and solutions for V2X communication in the upcoming 6G era to visualize the future of this research domain. This paper discusses the architecture and standards utilized in 6G enabled V2X communications and provisions a comprehensive analysis of V2X security in Confidentiality, Integrity, Availability, Authentication and Access Control (CIA 3 ) domains. Thereby, we analyze the impact of the emerging technological concepts of Blockchain and Federated Learning (FL) in 6G enabled V2X communication. Thereby, we suggest a Blockchain-enabled FL based generic security architecture for V2X communication in 6G networks. At the conclusive end, the review highlights key lessons learned and the future research directions in the domain of security of V2X communications in the 6G including; Privacy in 3D Fog Computing, Privacy in Augmented Reality, C. Secure Software Defined Networking (SDN), Physical Layer Security In THz Spectrum, SUMO (Simulation of Urban MObility) and Intrusion Detection using AI.

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Section: A V2x Communicationmentioning

confidence: 99%

Security of 6G-Enabled Vehicle-to-Everything Communication in Emerging Federated Learning and Blockchain Technologies

Kim,

Oh,

Yim

et al. 2024

IEEE Access

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“…It also shows how difficult it is for RNNs to learn and maintain longterm memory and that they are limited in their ability to influence data. Long-short-term memories (LSTMs) were suggested to overcome the vanishing gradient issue in [4,[14][15][16].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Peephole LSTM Neural Network-Based Channel State Estimators for OFDM 5G and Beyond Networks

et al. 2023

Self Cite

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This study uses deep learning (DL) techniques for pilot-based channel estimation in orthogonal frequency division multiplexing (OFDM). Conventional channel estimators in pilot-symbol-aided OFDM systems suffer from performance degradation, especially in low signal-to-noise ratio (SNR) regions, due to noise amplification in the estimation process, intercarrier interference, a lack of primary channel data, and poor performance with few pilots, although they exhibit lower complexity and require implicit knowledge of the channel statistics. A new method for estimating channels using DL with peephole long short-term memory (peephole LSTM) is proposed. The proposed peephole LSTM-based channel state estimator is deployed online after offline training with generated datasets to track channel parameters, which enables robust recovery of transmitted data. A comparison is made between the proposed estimator and conventional LSTM and GRU-based channel state estimators using three different DL optimization techniques. Due to the outstanding learning and generalization properties of the DL-based peephole LSTM model, the suggested estimator significantly outperforms the conventional least square (LS) and minimum mean square error (MMSE) estimators, especially with a few pilots. The suggested estimator can be used without prior information on channel statistics. For this reason, it seems promising that the proposed estimator can be used to estimate the channel states of an OFDM communication system.

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“…LSTM and GRU are two representative RNN variants and solve the problem of vanishing gradients faced by the original RNN structure. They have been adopted in network traffic problems with both univariate and multivariate time series forecasting problem formulations [7,27,28].…”

Section: Deep Learning-based Prediction Modelsmentioning

confidence: 99%

Internet Traffic Prediction with Distributed Multi-Agent Learning

Jiang

He²,

Gu³

2022

ASI

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Internet traffic prediction has been considered a research topic and the basis for intelligent network management and planning, e.g., elastic network service provision and content delivery optimization. Various methods have been proposed in the literature for Internet traffic prediction, including statistical, machine learning and deep learning methods. However, most of the existing approaches are trained and deployed in a centralized approach, without considering the realistic scenario in which multiple parties are concerned about the prediction process and the prediction model can be trained in a distributed approach. In this study, a distributed multi-agent learning framework is proposed to fill the research gap and predict Internet traffic in a distributed approach, in which each agent trains a base prediction model and the individual models are further aggregated with the cooperative interaction process. In the numerical experiments, two sophisticated deep learning models are chosen as the base prediction model, namely, long short-term memory (LSTM) and gated recurrent unit (GRU). The numerical experiments demonstrate that the GRU model trained with five agents achieves state-of-the-art performance on a real-world Internet traffic dataset collected in a campus backbone network in terms of root mean square error (RMSE) and mean absolute error (MAE).

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Deep Learning for Predicting Traffic in V2X Networks

Cited by 10 publications

References 29 publications

Security of 6G-Enabled Vehicle-to-Everything Communication in Emerging Federated Learning and Blockchain Technologies

Security of 6G-Enabled Vehicle-to-Everything Communication in Emerging Federated Learning and Blockchain Technologies

Deep Learning Peephole LSTM Neural Network-Based Channel State Estimators for OFDM 5G and Beyond Networks

Internet Traffic Prediction with Distributed Multi-Agent Learning

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