DSRC Applicability in Cameroon Road Traffic: A Simulation Study

Zongo, M.; Nlong, J.M.; Ndoundam, R.; Foerster, Angelika

doi:10.4108/eai.11-6-2021.170233

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…VANETs support a variety of interaction methods, including vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). A Basic Safety Message (BSM) [6], including the sender's actual location, speed, acceleration, and direction, as well as a temporary pseudonym to determine the sender, is broadcast every 100 milliseconds to assure communication among vehicles. However, because of the V2X connection, VANETs are an open-access environment that is much more vulnerable to misbehavior, causing significant safety problems.…”

Section: Introductionmentioning

confidence: 99%

One Versus All Binary Tree Method to Classify Misbehaviors in Imbalanced VeReMi Dataset

Slama,

Tarhouni,

Zidi

et al. 2023

IEEE Access

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Nowadays, transportation networks depend heavily on the technology known as vehicular ad hoc networks (VANETs). VANETs enhance traffic control and road safety while also enabling vehicle-tovehicle communication using basic safety messages (BSM), which are susceptible to different kinds of attacks. This study focuses on techniques for detecting and classifying misbehavior in VANETs while dealing with unbalanced data. In order to ensure equal treatment of minority and majority categories, we provide a novel method called One vs. All Binary Tree (OVA-BT). This approach separates binary classifiers for each kind of misbehavior and provides specific assessment metrics for each kind of misbehavior.We evaluate our experiment using five-fold cross-validation with six individual models of ML and an ensemble classifier. The findings demonstrated that the use of OVA-BT enhances the classification accuracy when compared to a traditional single multi-class model and that the classifier ensemble's classification performance is greater than the best individual model on the testing set.

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Section: Introductionmentioning

confidence: 99%

One Versus All Binary Tree Method to Classify Misbehaviors in Imbalanced VeReMi Dataset

Slama,

Tarhouni,

Zidi

et al. 2023

IEEE Access

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“…The main challenge in exchanging safety-critical messages is that the required latency is less than 100 ms [7][8][9] because of the high driving speeds of vehicles. Therefore, the safety messages have to be disseminated with minimum delay.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Mobile Road Side Units in VANETs

Karunathilake

Förster

2022

Vehicles

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The number of vehicles on the road increases daily, causing many fatal accidents and wasting much time for the average commuter every day due to congestion. Vehicular ad hoc networks (VANETs) were introduced to overcome these issues by enabling vehicle-to-vehicle communication and vehicle-to-infrastructure communication. The prime challenge in VANETs is the necessity of very low communication delays, especially for safety-related applications due to the high mobility nature of vehicles. The VANET architecture introduces a network component, the Road Side Unit (RSU), to meet the required delay limitations. Even though the RSU is a critical component in VANETs, as expected, the RSUs were not deployed throughout the world because of their high investment cost. As a solution, the idea of mobile RSU (mRSU) was introduced, and, ever since, several techniques of mRSU deployment strategies have been proposed. In this survey, we first analyze the importance of the RSU to the VANET architecture with real-world data incorporating the new 5G standard. Then, we investigate the research done in the areas of mRSU and exploit the pros and cons of each mRSU deployment strategy. Finally, we also discuss the future research directions of mRSU, and we explain the challenges connected to these future trends.

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“…1. The interaction between vehicles (V2V) is ensured by broadcasting every 100 ms a Basic Safety Messages (BSMs) [13] , according to the SAE J2735 standard, containing the current position, acceleration, speed, heading, and other details of each vehicle. The basic purpose of vehicular networks is to ensure passenger safety, conductors, and vehicles through the exchange of private accident and traffic information among vehicles [14].…”

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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DSRC Applicability in Cameroon Road Traffic: A Simulation Study

Cited by 5 publications

References 18 publications

One Versus All Binary Tree Method to Classify Misbehaviors in Imbalanced VeReMi Dataset

One Versus All Binary Tree Method to Classify Misbehaviors in Imbalanced VeReMi Dataset

A Survey on Mobile Road Side Units in VANETs

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

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