Survey on Artificial Intelligence (AI) techniques for Vehicular Ad-hoc Networks (VANETs)

Mchergui, Abir; Moulahi, Tarek; Zeadally, Sherali

doi:10.1016/j.vehcom.2021.100403

Cited by 88 publications

(48 citation statements)

References 126 publications

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“…The motivation behind such approach lies in the ability and high potential of these methods to solve various challenges in ad hoc networks ( MANET, VANET, FANET, ...) such as end-to-end delay, packet delivery rate, route stability, energy consumption and routing overhead [15]- [17]. With all this in mind, paper [18] presents a detailed survey of the main advantages and areas of use of the above techniques in VANET.…”

Section: A Intelligent Routingmentioning

confidence: 99%

An Intelligent Machine Learning Based Routing Scheme for VANET

et al. 2022

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Today, Vehicular Ad-hoc Networks (VANET) have become an interesting research topic for developing Intelligent Transport Systems. In urban environments, vehicles move continuously and at different speeds, which leads to frequent changes in the network topology. The main issue faced in an urban scenario is the performance of routing protocols when delivering data from one vehicle to another. This paper introduces ECRDP, an Efficient Clustering Routing approach using a new clustering algorithm based on Density Peaks Clustering (DPC) and Particle Swarm Optimization (PSO). First, the PSO algorithm is applied to determine the cluster heads, or a new fitness function for finding the best solutions is formulated using the DPC algorithm. Next, clustering is performed based on the reliability of links parameter between vehicles. Then, a maintenance phase is proposed to update the cluster heads and redistribute the vehicles in the clusters. Finally, the effectiveness of the suggested scheme is evaluated by a simulation operated by MATLAB on a real urban scenario. The results achieved show an overall increase in stability, proven by a reduction in change rate by 74%, and an improvement in performance indicated by an increase in intracluster throughput by 34% and inter-cluster by 47%, as well as an overall reduction of average delay by 16%.

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Section: A Intelligent Routingmentioning

confidence: 99%

An Intelligent Machine Learning Based Routing Scheme for VANET

et al. 2022

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“…One Q-value estimate tells the other one what to do. We can get unbiased Q-value forecasts for the other estimator's activities by using unbiased estimators that are not biased [18]. Keeping updates separate from wrong assumptions helps get rid of bias.…”

Section: Double Dueling Dqnmentioning

confidence: 99%

XSS filter evasion using reinforcement learning to assist cross-site scripting testing

Mondal

Banerjee

Gupta

2022

ijhs

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Machine learning and deep learning are widely utilized and highly effective in attack classifiers. Little research has been undertaken on detecting and protecting cross-site scripting, leaving artificial intelligence systems susceptible to adversarial assaults (XSS). It is crucial to develop a mechanism for increasing the algorithm's resilience to assault. This study intends to utilize reinforcement learning to enhance XSS detection and adversarial combat attacks. Before mining the detection model's hostile inputs, the model's information is extracted using a reinforcement learning framework. Second, the detection technique is simultaneously trained using an adversarial strategy. Every cycle, the classification method is educated with freshly discovered harmful data. The proposed XSS model effectively mines destructive inputs missed by either black-box or white-box detection systems during the experimental phase. It is possible to train assault and detection models to enhance their capacity to protect themselves, leading to a lower rate of escape due to this training.

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“…The synergy between AI and 6G is expected to take this research into the next level, and allow large scale adoption of these technologies (Mchergui et al 2021;Marr, 2021;Pao, 2021).…”

Section: Transportationmentioning

confidence: 99%

Synergy between 6G and AI: Open Future Horizons and Impending Security Risks

Yaacoub¹

2022

Preprint

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<p>This paper investigates the synergy between 6G and AI. It argues that they can unlock future horizons, by discussing how they can address future challenges in healthcare, transportation, virtual reality, education, resource management, robotics, in addition to public safety and warfare. However, these great opportunities come also with greater risk. Therefore, the paper provides an overview of the security risks and challenges, along with possible mitigation techniques. <br></p>

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Survey on Artificial Intelligence (AI) techniques for Vehicular Ad-hoc Networks (VANETs)

Cited by 88 publications

References 126 publications

An Intelligent Machine Learning Based Routing Scheme for VANET

An Intelligent Machine Learning Based Routing Scheme for VANET

XSS filter evasion using reinforcement learning to assist cross-site scripting testing

Synergy between 6G and AI: Open Future Horizons and Impending Security Risks

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