SP-TRUST: a trust management model for speed trust in vehicular networks. Issue 11 (2nd November 2022)

• Record Type: Journal Article
• Authors: Souissi, Ilhem; Ben Azzouna, Nadia; Abidi, Rihab; Berradia, Tahar; Ben Said, Lamjed
• Is Part Of: International journal of computers and applications. Volume 44:Issue 11(2022)
• Page Start: 1065
• Page End: 1073
• Publication Date: 2022-11-02
• Subjects: VANETs -- trust model -- vehicle speed verification -- fuzzy logic theory; Computers -- Periodicals; Computer software -- Periodicals; Computer networks -- Periodicals; Multimedia systems -- Periodicals; Internet -- Periodicals; World Wide Web -- Periodicals; Minicomputers -- Periodicals; Microcomputers -- Periodicals; 004.05
• DOI: 10.1080/1206212X.2022.2108562
• Languages: English
• ISSNs: 1206-212X

Abstract

ABSTRACT: Information security mechanisms are crucial for Vehicular Ad-hoc NETworks (VANET) applications in order to preserve their robustness. The efficiency of these applications relies on the reliability of the used information, especially the vehicle-related information such as location and speed. Trust management models are crucial to evaluate the quality of the used information. Accordingly, we focus in this paper on the assessment of speed information to detect the malicious vehicles using a fuzzy-based model (SP-TRUST). The proposed model relies on two fuzzy inference systems. The first one evaluates the speed trust based on traffic rules (inter-vehicle distance) while considering the road topology (angle deviation) and the traffic state (density). The correlation between these parameters and the speed value is assessed. The second inference system assesses the speed trust based on the behavior of neighbor vehicles using the median speed of vehicles. Simulations were carried out to evaluate the robustness and the scalability of SP-TRUST model. The results of the experimental studies proved that the model performs well in detecting different behaviors of malicious vehicles in different scenarios, especially when the percentage of malicious vehicles is lower than 50%.