Network Traffic Prediction Model Considering Road Traffic Parameters Using Artificial Intelligence Methods in VANET

Sepasgozar, Sanaz Shaker; Pierre, Samuel

doi:10.1109/access.2022.3144112

Cited by 45 publications

(20 citation statements)

References 36 publications

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“…In this study, the stationarity of time series was confirmed using the augmented Dicky-Fuller (ADF) test as the nonstationary models can yield misleading results, as observed in earlier studies [24,27]; although, LSTM can be used to model a nonstationary time series. Moreover, we normalized the time series variances using the Box-Cox power transformation.…”

Section: Resultssupporting

confidence: 52%

“…Several studies have analyzed the effectiveness and superiority of the LSTM process for bandwidth forecasting [21][22][23][24][25][26][27][28][29][30]. For instance, in [28] the researchers investigated the performance of different ML techniques and assessed the forecast performances of their video over the internet.…”

Section: Related Workmentioning

confidence: 99%

“…They noted that their hybrid model showed a better root mean square error (RMSE) value. In different studies [27,30], the authors used LSTM for forecasting the vehicular ad hoc network (VANET). They determined the forecasting accuracy with the help of the RMSE and mean absolute percentage error (MAPE); the results proved the effectiveness of the proposed mechanism.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts

Hamdi

Hamam

2022

Sensors

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Recently, there has been an increasing need for new applications and services such as big data, blockchains, vehicle-to-everything (V2X), the Internet of things, 5G, and beyond. Therefore, to maintain quality of service (QoS), accurate network resource planning and forecasting are essential steps for resource allocation. This study proposes a reliable hybrid dynamic bandwidth slice forecasting framework that combines the long short-term memory (LSTM) neural network and local smoothing methods to improve the network forecasting model. Moreover, the proposed framework can dynamically react to all the changes occurring in the data series. Backbone traffic was used to validate the proposed method. As a result, the forecasting accuracy improved significantly with the proposed framework and with minimal data loss from the smoothing process. The results showed that the hybrid moving average LSTM (MLSTM) achieved the most remarkable improvement in the training and testing forecasts, with 28% and 24% for long-term evolution (LTE) time series and with 35% and 32% for the multiprotocol label switching (MPLS) time series, respectively, while robust locally weighted scatter plot smoothing and LSTM (RLWLSTM) achieved the most significant improvement for upstream traffic with 45%; moreover, the dynamic learning framework achieved improvement percentages that can reach up to 100%.

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Section: Resultssupporting

confidence: 52%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts

Hamdi

Hamam

2022

Sensors

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“…In [12], the author proposed a model integrating a Random Forest-Gated Recurrent Unit-Network Traffic Prediction algorithm (RF-GRU-NTP). The model is divided into three stages of network traffic prediction based on three different communications.…”

Section: Related Workmentioning

confidence: 99%

Traffic prediction with deep learning for vehicular communication network

Wang

Liu

2022

International Conference on Smart Transportation and City Engineering (STCE 2022)

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Nowadays, the construction of Internet of vehicles provides a lot of convenience for urban public safety and transportation. Among them, the prediction of traffic flow based on traffic road condition prediction is very important. Due to the interference of many external factors, such as unexpected events and weather, this problem is difficult. In recent years, due to its strong learning ability, deep learning has made many breakthroughs in various image classification and data analysis and processing problems. Among them, the traffic flow prediction method based on deep learning models can provide a set of rich tools, which can make good use of these data to build the Internet of vehicles. In this paper, we compare the deep learning model with the traditional model, and further attempt the preprocessing methods such as Wavelet and Singular Spectrum Analysis (SSA) to improve the performance of the deep learning model. Based on a real International Mobile Equipment Identity (IMEI) data set, we evaluated LSTM, GRU, ARIMA, SVR and ANN models. Considering the problem of whether the deep learning method is suitable for real-time traffic prediction, we counted and compared the training preprocessing time. We found that compared with LSTM and GRU, ARIMA is still competitive and Wavelet or SSA is not helpful as we expected.

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“…VANETs [5,6] are composed by different infrastructures: a vehicle which equipped by an OnBoard Unit (OBU) that regularly broadcasts their status to its neighbors, a Road-side Unit (RSU) [7] is an infrastructure installed on the side of the road that communicates with nearby communicating OBUs by broadcasting traffic information, weather conditions, and helping OBUs connect to the Internet. Another infrastructure called Central Authority (CA) providing services such as registration and management of all network entities (OBU and RSU) [8] as well as the revocation of certificates in the event of misbehavior [9].…”

Section: Introductionmentioning

confidence: 99%

Towards Misbehavior Intelligent Detection Using Guided Machine Learning in Vehicular Ad-hoc Networks (VANET)

Slama¹,

Alaya²,

Zidi³

2022

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C-ITS (Cooperative Intelligent Transport Systems) est une nouvelle technologie qui contribue à la réduction des accidents de la circulation et à l'amélioration de la sécurité routière. VANET ( V ehicular A d hoc Networks) sont un système STI basé sur la communication inter-véhicules par la transmission de messages de sécurité de base (BSM), qui sont vulnérables à une variété de comportements inappropriés. Pour résoudre ce défi, nous avons développé dans cet article un système de détection de mauvais comportement (MDS) basé sur une approche d'apprentissage automatisé pour identifier et catégoriser les messages de mauvais comportement délivrés par un véhicule sur les VANET à l'aide de la base de données d'extension VeReMi. Cette étude examine différents types de classification : dans la classification binaire, toutes sortes d'inconduites ont été regroupées en une seule catégorie « inconduite » ; cependant, dans la classification multi-classes pour trois classes, la mauvaise conduite a été divisée en deux classes : les attaques et les fautes. Le classificateur a des problèmes substantiels lors de l'apprentissage à partir de données déséquilibrées wLorsque vous travaillez avec des problèmes multi-classes, cela devient considérablement plus complexe. Les relations entre les catégories ne sont plus bien définies et il est facile de perdre en efficacité dans une classe tout en s'améliorant dans une autre. En conséquence, les résultats ne sont pas cohérents dans l'approche d'apprentissage classique pour la classification multi-classes lors de la classification des comportements répréhensibles dans différents types de classes de comportements répréhensibles. Pour résoudre ce problème, nous avons développé une approche nouvelle et puissante appelée "Approche d'apprentissage guidé pour la classification multi-classes" pour réduire le nombre de classes en combinant des comportements inappropriés comparables en un seul. Selon les résultats, le classificateur Random Forest surpasse les autres classificateurs.

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Network Traffic Prediction Model Considering Road Traffic Parameters Using Artificial Intelligence Methods in VANET

Cited by 45 publications

References 36 publications

Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts

Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts

Traffic prediction with deep learning for vehicular communication network

Towards Misbehavior Intelligent Detection Using Guided Machine Learning in Vehicular Ad-hoc Networks (VANET)

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