Abstract
The Internet of vehicles (IoV) depicts a reality where ordinary things are connected to vehicular ad-hoc networks (VANETs), allowing them to transmit and collaborate. By placing these regular objects in VANETs and making them available at any time, this network and data sharing may raise real privacy and security issues. Thus, group-based communication is mostly preferred in the literature. However, in heavy network scenarios, cluster-based communication mostly leads to additional overload in the form of the group leader that causes delay and disrupts the performance of a network. Due to the interaction of VANETs with applications that are not stable for life, privacy and security mechanism for detecting many malicious nodes is in great demand. Therefore, a multi-phantom node selection has been proposed in this paper to select trustworthy, normal, and malicious nodes. The multi-phantom node scheme is proposed to reduce the phantom node load, where the multi-lateral nodes in a cluster act as a phantom node to share the load. A multi criteria decision-making (MCDM) methodology (analytic network process) is used to optimize the phantom node to pre-serve privacy using the privacy preserved trust relationship (PTR) model. The results show checking the stability of parameters and using sensitivity analysis by considering different scenarios for the most optimal phantom node to preserve vehicle location privacy. The impact of the proposed model will be more clearly visible in its real-time implementation in urban areas vehicle networks.
Highlights
In 2001, the vehicular ad-hoc networks (VANETs) were first described and introduced [1] under “car-to-car-ad-hoc mobile communication applications,” which enable to form networks and relay information between cars
In VANETs, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) transmission systems coexist to deliver security, safety, road protection, emergency services, infotainment, navigation, and payment service and permit the vehicles to share information. These applications allow nodes to connect with infrastructure networks, citizens, and the network, which leads VANETs to become the fundamental paradigm known as the Internet of vehicles (IoVs) [2,3]
analytical network process (ANP) has been used in a variety of applications such as cluster head selection in wireless sensor networks [6], source location privacy preservation [7], controller selection in software-defined networks [8], forwarder selection in VANETs [9] and many more
Summary
In 2001, the VANETs were first described and introduced [1] under “car-to-car-ad-hoc mobile communication applications,” which enable to form networks and relay information between cars. In VANETs, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) transmission systems coexist to deliver security, safety, road protection, emergency services, infotainment, navigation, and payment service and permit the vehicles to share information. These applications allow nodes to connect with infrastructure networks, citizens, and the network, which leads VANETs to become the fundamental paradigm known as the Internet of vehicles (IoVs) [2,3]. Malevolent vehicles are entitled as intruder, adversary, malicious and rogue vehicles that execute malevolent events such that signal sniffing, pattern sniffing, altering of information of packets, dropping packets, etc These intruder, adversary, malicious and rogue entities from disturbing the normal network activities, numerous anomalies, signature, watchdog, cross-layer, and honeypot-based intrusion detection system (IDS) mechanisms are proposed but each has its limitations
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