A New Fuzzy Logic Based Model for Location Trust Estimation in Electric Vehicular Networks

Souissi, Ilhem; Azzouna, Nadia Ben; Berradia, Tahar; Saïd, Lamjed Ben

doi:10.1007/978-3-030-15032-7_29

Cited by 1 publication

(2 citation statements)

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“…Fuzzy logic focuses on representing the imprecision of human reasoning for decision making in an imprecise and uncertain environment [95]. In this subsection, trust management models employing fuzzy logic have been presented in detail [96][97][98][99][100]. Guleng et al [96] presented a decentralized trust management framework that employs fuzzy logic to amalgamate a vehicle's direct experience and the recommendations of its peers towards a target vehicle in order to tag the unintended dishonest behavior of the said target vehicle.…”

Section: Fuzzy Logic-based Trust Modelsmentioning

confidence: 99%

“…Souissi et al [97] proposed a model that amalgamates trust, in terms of the degree of similarity, and fuzzy logic to guarantee legitimate location information. The central authority validates the location information by cross checking the attributes of the reported lane and the reporting vehicle prior to storing it locally for future reference, e.g., optimum route selection and the status of road traffic, etc.…”

Section: Fuzzy Logic-based Trust Modelsmentioning

confidence: 99%

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A Survey of Trust Management in the Internet of Vehicles

et al. 2021

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Over the past decade, the groundbreaking technological advancements in the Internet of Vehicles (IoV) coupled with the notion of trust have attracted increasing attention from researchers and experts in intelligent transportation systems (ITS), wherein vehicles establish a belief towards their peers in the pursuit of ensuring safe and efficacious traffic flows. Diverse domains have been taking advantage of trust management models in the quest of alleviating diverse insider attacks, wherein messages generated by legitimate users are altered or counterfeited by malicious entities, subsequently, endangering the lives of drivers, passengers, and vulnerable pedestrians. In the course of vehicles forming perceptions towards other participating vehicles, a range of contributing parameters regarding the interactions among these vehicles are accumulated to establish a final opinion towards a target vehicle. The significance of these contributing parameters is typically represented by associating a weighting factor to each contributing attribute. The values assigned to these weighting factors are often set manually, i.e., these values are predefined and do not take into consideration any affecting parameters. Furthermore, a threshold is specified manually that classifies the vehicles into honest and dishonest vehicles relying on the computed trust. Moreover, adversary models as an extension to trust management models in order to tackle the variants of insider attacks are being extensively emphasized in the literature. This paper, therefore, reviews the state of the art in the vehicular trust management focusing on the aforementioned factors such as quantification of weights, quantification of threshold, misbehavior detection, etc. Moreover, an overarching IoV architecture, constituents within the notion of trust, and attacks relating to the IoV have also been presented in addition to open research challenges in the subject domain.

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