Effect of security and trustworthiness for a fuzzy cluster management system in VANETs. (June 2019)

• Record Type: Journal Article
• Authors: Bylykbashi, Kevin; Elmazi, Donald; Matsuo, Keita; Ikeda, Makoto; Barolli, Leonard
• Is Part Of: Cognitive systems research. Volume 55(2019)
• Page Start: 153
• Page End: 163
• Publication Date: 2019-06
• Subjects: Security -- Trustworthiness -- VANETs -- Fuzzy logic -- Clustering; Cognition -- Periodicals; Cognitive engineering (System design) -- Periodicals; Artificial intelligence -- Periodicals; 153.05
• DOI: 10.1016/j.cogsys.2019.01.008
• Languages: English
• ISSNs: 1389-0417

Abstract

Abstract: Recently, smart cities and Internet of Things (IoT) applications, such as Vehicular Ad-hoc Networks (VANETs) and Opportunistic networks have been deeply investigated. However, these kinds of wireless networks have security problems. Also, the vehicles can be not trustworthy, which brings different communication problems. In this work, we present a Fuzzy Cluster Management System (FCMS) for VANETs. We present and compare two fuzzy-based system models: FCMS1 and FCMS2 for clustering of vehicles in VANETs. For FCMS1, we use three input parameters: Vehicle Relative Speed with Vehicle Cluster (VRSVC), Vehicle Degree of Centrality (VDC) and Vehicle Security (VS). The output parameter is Vehicle Remain or Leave Cluster (VRLC). For FCMS2, we consider four input parameters by adding Vehicle Trustworthiness (VT) as a new parameter. We evaluate both systems by simulations. The simulation results show that vehicles with the same VRSVC and with high VDC, VS and VT values have higher possibility to remain in the cluster. By comparing FCMS1 and FCMS2, we found that the FCMS2 can manage better the vehicles in the cluster than FCMS1.