A Novel Secured Multi-Access Edge Computing based VANET with Neuro fuzzy systems based Blockchain Framework

Poongodi, M.; Bourouis, Sami; Ahmed, Ahmed Najat; Vijayaragavan, M.; K.G.S., Venkatesan; Alhakami, Wajdi; Hamdi, Mounir

doi:10.1016/j.comcom.2022.05.014

Cited by 52 publications

(3 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In response, the traffic light is controlled to reduce traffic congestion. Similarly, RFID-based traffic congestion control was presented in reference [14] for emergency vehicle routing applications. Anomaly detection began with the Principal Component Analysis (PCA) method, which required extensive prior knowledge of link observations.…”

Section: Literature Reviewmentioning

confidence: 99%

AI-Based Efficient Wireless Technologies and Infrastructure-Based Networks With VANET For Smart Transportation High Performance

Xiong¹,

Li²,

Hou³

et al. 2023

Preprint

View full text Add to dashboard Cite

VANETs (Vehicular Ad-hoc NETworks) were deemed most suitable communication network for supporting the dissemination of alert messages due to their low dissemination delays as well as extensive vehicle coverage in vicinity of an emergency. With the introduction of cooperative ITS services, it is envisaged that emerging vehicular networks will progressively rely on Vehicle to Infrastructure (V2I) communication lines, which are expected to be nominally accessible with certain temporary as well as time-limited connectivity losses. This study proposes a novel method for VANET-based efficient vehicle clustering and routing based on network infrastructure for high-performance smart transportation. the vehicle clustering using infrastructure-based fuzzy K-means convolutional neural networks. then the energy-efficient cluster-based multi-hop distributed routing. the experimental analysis in terms of latency, network lifetime, throughput, QoS, energy efficiency, and packet delivery ratio. In addition, empirical equations that can be used to predict speed recommendations for drivers are derived from the result.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

AI-Based Efficient Wireless Technologies and Infrastructure-Based Networks With VANET For Smart Transportation High Performance

Xiong¹,

Li²,

Hou³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Avoiding potentially dangerous interactions with hostile vehicle users is made easier using the batch authentication and key exchange approach proposed by Poongodi et al [21]. A Public Key Infrastructure (PKI), an ID-based system, and a MAC-based system are also proposed.…”

Section: Related Workmentioning

confidence: 99%

An Improved ARO Model for Task Offloading in Vehicular Cloud Computing in VANET

Thirumalraj,

2023

Preprint

View full text Add to dashboard Cite

Vehicle-to-vehicle (V2V) communication enables a network of automobiles to perform collaborative computing, giving rise to the concept of a "vehicular cloud" (VC). However, without the need for edge nodes or cloud servers, vehicles can carry out applications needing the massive amount of processing cooperatively on their own by creating a Vehicular Ad-Hoc Network (VANET). Managing the recurrent topology alteration caused by vehicle mobility is a significant challenge for VANET cooperative computing. In this research, we present a V2V-based cooperative computing approach. The suggested method takes into account the distance between vehicles while choosing which ones to collaborate with, and it waits task offloading until the last possible moment to ensure a stable and energy-efficient cooperative computing environment. Despite its competitive performance when compared to other MH algorithms, the artificial rabbits optimisation (ARO) algorithm still suffers from poor accuracy and the issue of becoming trapped in solutions. By antagonism methods, this research creates selective opposition version of the artificial rabbit procedure (LARO), which eliminates the negative consequences of these shortcomings. To begin, during the random concealment phase, a Lévy flight strategy is implemented to increase population diversity and dynamics. The algorithm's convergence accuracy is enhanced by the richness of its various population samples. The tracking efficiency is improved, and ARO is kept from getting stuck in its existing local solutions by adopting the selective opposition technique. In comparison to traditional static scheduling techniques, the suggested strategy improves upon both energy efficiency and network reliability.

show abstract

“…We investigate the setting for the adversary network for the poison attack, the assumption, the first case, taken as perfect knowledge gained by the adversary on the target classifier ( P TC ) and known feature space t ( x ).The second case, is that adversaries gained the less or limited knowledge ( L TC ), target classifier. We assumed that attacker may have knowledge of features representation, but not the training dataset (Rathore et al, 2022; Poongodi, Bourouis et al, 2022; Ramesh, Lihore et al, 2022; Poongodi, Malviya, Hamdi et al, 2022; Poongodi, Malviya, Kumar et al, 2022; Poongodi, Hamdi, & Wang 2022; Poongodi et al, 2021; Ramesh, Vijayaragavan et al, 2022; Hamdi et al, 2022; Poongodi, Hamdi, Malviya et al, 2022; Kamruzzaman 2021; Hossain et al, 2022; Chen et al, 2019; Kamruzzaman 2013, 2014; Zhang et al, 2021; Hossain, Kamruzzaman et al, 2022; Sarker et al, 2021; Shi et al, 2020; Chen et al, 2020).…”

Section: Attack Modelsmentioning

confidence: 99%

SVM‐based generative adverserial networks for federated learning and edge computing attack model and outpoising

et al. 2022

Self Cite

View full text Add to dashboard Cite

Machine learning are vulnerable to the threats. The Intruders can utilize the malicious nature of the nodes to attack the training dataset to worsen the process and manipulate the learning and make the over all system with less efficiency and performance. The optimized poison attack procedures are already introduced to estimate the overall bad scenario, design the intrusion as bi‐level optimization and it is considered computational complexity is high and demanding, in contrary the applicability is limited such models deep neural networks. In this research papers, we have proposed, novel proposed system, poisoning attacks against the Machine learning training dataset, including the genuine data points that reduce the accuracy of the classifier in the process of training. The proposed system have 3 components of Generative Adverserial networks (GAN) generator, discriminator, and the target classifier. The proposed system allows to detect the vulnerability easy and it can be found as similar as realistic attacks to detect the area where the underlying data distribution have more possibility of poising attack which cause vulnerability to the network. Our experimentation, proves the claim our that the proposed model is effective on compromising the classifiers uses the machine learning algorithms and also deep learning networks.

show abstract

A Novel Secured Multi-Access Edge Computing based VANET with Neuro fuzzy systems based Blockchain Framework

Cited by 52 publications

References 44 publications

AI-Based Efficient Wireless Technologies and Infrastructure-Based Networks With VANET For Smart Transportation High Performance

AI-Based Efficient Wireless Technologies and Infrastructure-Based Networks With VANET For Smart Transportation High Performance

An Improved ARO Model for Task Offloading in Vehicular Cloud Computing in VANET

SVM‐based generative adverserial networks for federated learning and edge computing attack model and outpoising

Contact Info

Product

Resources

About