A Decentralized Blockchain-Based Trust Management Framework for Vehicular Ad Hoc Networks

Gazdar, Tahani; Alboqomi, Ohoud; Munshi, Asmaa

doi:10.3390/smartcities5010020

Cited by 11 publications

(7 citation statements)

References 26 publications

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“…Tahani et al. [ 35 ] suggested a trust assessment scheme relying on blockchain technology that takes into consideration only the reliability of the events to calculate the pairwise local trust among vehicles. Once local trusts from all monitoring vehicles have been computed, the global trust for each vehicle is computed by aggregating the initial trust and the sum of all local trusts assigned to a particle vehicle prior to averaging it out.…”

Section: Related Workmentioning

confidence: 99%

Towards a Machine Learning Driven Trust Management Heuristic for the Internet of Vehicles

Siddiqui

Mahmood

Sheng

et al. 2023

Sensors

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The rapid proliferation of the emerging yet promising notion of the Internet-of-Vehicles (IoV) has led to the development of a variety of conventional trust assessment schemes to tackle insider attackers. The primary reliance of these frameworks is on the accumulation of individual trust attributes. While aggregating these influential parameters, weights are often associated with each individual attribute to reflect its impact on the final trust score. It is of paramount importance that such weights be precise to lead to an accurate trust assessment. Moreover, the value of the minimum acceptable trust threshold employed for the identification of dishonest vehicles needs to be carefully defined to avoid delayed or erroneous detection. This paper employs an IoT data set from CRAWDAD by suitably transforming it into an IoV format. This data set encompasses information regarding 18,226 interactions among 76 nodes, both honest and dishonest. First, the influencing parameters (i.e., packet delivery ratio, familiarity, timeliness and interaction frequency) were computed, and two feature matrices were formed. The first matrix (FM1) takes into account all the pairwise individual parameters as individual features, whereas the second matrix (FM2) considers the average of all pairwise computations performed for each individual parameter as one feature. Subsequently, unsupervised learning is employed to achieve the ground truth prior to applying supervised machine learning algorithms for classification purposes. It is worth noting that Subspace KNN yielded a perfect precision, recall, and the F1-score equal to 1 for individual parametric scores, whereas Subspace Discriminant returned an ideal precision, recall, and the F1-score equal to 1 for mean parametric scores. It is also evident from extensive simulations that FM2 yielded more accurate classification results compared to FM1. Furthermore, decision boundaries among honest and dishonest vehicles have also been computed for respective feature matrices.

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Section: Related Workmentioning

confidence: 99%

Towards a Machine Learning Driven Trust Management Heuristic for the Internet of Vehicles

Siddiqui

Mahmood

Sheng

et al. 2023

Sensors

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“…There are still a number of challenges they face when it comes to fleet telematics. It is imperative for fleet managers and drivers to have a high level security and reliable Intelligent Transportation System (ITS) in light of increasing traffic and air pollution [11]. IoT sensors gather data about the environment and also help to enhance ITS.…”

Section: Internet Of Vehiclesmentioning

confidence: 99%

An approach to preventing vehicular ad-hoc networks from malicious nodes based on blockchain

Azath¹,

Singh²

2023

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This study presents a revolutionary blockchain-based security mechanism that authenticates vehicles and instantly alerts other vehicles to unlawful messages. Access should only be allowed to authenticated vehicles in order to increase the network security, and access should be restricted or revoked for any vehicles that exhibit inappropriate behaviour. Vehicles linked to the Vehicular Ad Hoc Network (VANET) can share data and take immediate action with the help of adjacent infrastructure devices, like streetlights and traffic signals, as well as with one another. In order to maintain efficiency and safety, an intelligent transportation system depends on the flow of information between vehicles. Intelligent vehicles are vulnerable to hacking attacks because they have numerous technical flaws. A proposed security scheme identifies malicious nodes and detects forged messages, based on multiple factors like sender reputation, time and distance effectiveness. This is accomplished by employing environmental sensory data and authenticating the packets, allowing a vehicle to add numerous blocks to the blockchain. Research has been conducted on the bases of safety protection for vehicle systems as a result of security concerns. When the proposed approach is tested on different vehicle and attacker densities, the results indicate that it has a lower authentication delay and higher communication overhead than the other existing approaches. This study explored various threats from malicious nodes and the techniques in which our proposed methodology handles such attacks.

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“…It could effectively resolve the issues that developed in centralized structures and solved trust problems that affected the entities. Gazdar et al [24] solved these problems by presenting a decentralized BC-based Trust Management Framework (BC-TMF) that calculated trust metrics for vehicles. These trust metrics depended on the messages' authenticity.…”

Section: Related Workmentioning

confidence: 99%

Blockchain with Quantum Mayfly Optimization-Based Clustering Scheme for Secure and Smart Transport Systems

Alamro,

Alqahtani,

Alruwaili

et al. 2023

Sustainability

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Blockchain (BC) with a clustering scheme can be used to build secure and smart Vehicular Ad-Hoc Networks (VANETs), which provide improved data integrity, enhanced security, efficient resource allocation, and streamlined processes. This technology has revolutionized the transport industry by enabling safer, more efficient, and transparent transportation networks. Therefore, this paper concentrates on the design of a new Blockchain with a Quantum Mayfly Optimization-based Clustering Scheme for Secure and Smart Transport Systems (BQMFO-CSSTS) technique. The objective of the presented BQMFO-CSSTS technique is to build a secure VANET via a BC-based technology and clustering process. Moreover, the BQMFO-CSSTS technique initially uses a Quantum Mayfly Optimization (QMFO) system with a fitness function for the selection of cluster heads (CHs) and the cluster construction process. In addition, BC technology is used as trust infrastructure to provide trustworthy services to the user and protect the privacy of the CHs and cluster members (CMs). The proposed scheme leverages the decentralized and immutable nature of BC to establish trust and ensure the integrity of cluster formation in VANETs. Finally, the BQMFO-CSSTS technique uses trajectory similarity metrics to protect the integrity of the CMs against attacks. The simulation results of the BQMFO-CSSTS technique are validated using a series of measures. The comprehensive results reported the superior outcomes of the BQMFO-CSSTS method over other recent approaches, with the maximum throughput being 1644.52 kbps. Therefore, integration of BC technology provides a transparent and secure framework through which to manage cluster membership, data sharing, and trust establishment among vehicles.

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A Decentralized Blockchain-Based Trust Management Framework for Vehicular Ad Hoc Networks

Cited by 11 publications

References 26 publications

Towards a Machine Learning Driven Trust Management Heuristic for the Internet of Vehicles

Towards a Machine Learning Driven Trust Management Heuristic for the Internet of Vehicles

An approach to preventing vehicular ad-hoc networks from malicious nodes based on blockchain

Blockchain with Quantum Mayfly Optimization-Based Clustering Scheme for Secure and Smart Transport Systems

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