A Survey of Trust Management in the Internet of Vehicles

Abstract

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.

A Fog Computing Model for VANET to Reduce Latency and Delay Using 5G Network in Smart City Transportation

“…Siddiqui et al [29] provided an in-depth examination of state-of-the-art trust management models in the IoV that utilize a variety of computational domains. Their research focused on evaluating trust management methods in terms of the evaluation tools used, the weights applied during trust aggregation, misbehavior detection, attack resistance, and quantification of the misbehavior detection threshold set.…”

An Anonymous Certificateless Signcryption Scheme for Secure and Efficient Deployment of Internet of Vehicles

“…The 𝕙 𝔼 𝕔 is the compressed form of 𝔼𝕔, which contains fewer key and parameters size [30,31]. Equation (1) represents the 𝕙𝔼𝕔 of genus 𝒢≥ 2 over a finite field 𝒰𝑝, where 𝒢 is the non-intersecting curves that is not touching each other when it is drawn on surface.…”

“…𝕙𝔼𝕔: 𝑄 2 +H(𝑉)𝑄= 𝐹(𝑉) mod 𝑝 (1) where 𝐻(𝑉) and 𝐹(𝑉) are polynomials with coefficients in 𝒰𝑝. So, the degree of H(𝑉) at most 𝒢 and the degree of 𝐹(𝑉) is equal to 2𝒢 + 1.…”

“…Over the past few decades, academic and industry researchers have worked harder to push the technology of Mobile Ad hoc Networks (MANETs) to a new extreme [1]. Mobile devices may now establish a network with flying, self-organizing, and dynamic connections to one another without the need for any fixed communication infrastructure [2].…”

Towards a Novel Air–Ground Intelligent Platform for Vehicular Networks: Technologies, Scenarios, and Challenges

“…With the integration of modernized technologies such as IoT and various new communication modes, including Vehicle-to-Vehicle (V2V), Vehicle-to-Road Side Units (V2R), Vehicle-to-Infrastructure of Cellular Networks (V2I), Vehicle-to-Sensors(V2S), and Vehicleto-Person (V2P), IoV can expand the traditional Vehicular Ad hoc Networks (VANETs) capability by adding several new services and applications. IoV is able to support various functions, including intelligent traffic management, traffic safety enhancements, dynamic information services, intelligent vehicle control for improving the overall road experience of users [2][3][4][5]. However, vehicle onboard computation capacity is falling short when new latency-critical and data-intensive applications are considered; hence, new challenges are arising in VNs.…”