A Trust Model Using Edge Nodes and a Cuckoo Filter for Securing VANET under the NLoS Condition

Abstract

Trust, as a key element of security, has a vital role in securing vehicular ad-hoc networks (VANETs). Malicious and selfish nodes by generating inaccurate information, have undesirable impacts on the trustworthiness of the VANET environment. Obstacles also have a negative impact on data trustworthiness by restricting direct communication between nodes. In this study, a trust model based on plausibility, experience, and type of vehicle is presented to cope with inaccurate, incomplete and uncertainty data under both line of sight (LoS) and none-line of sight (NLoS) conditions. In addition, a model using the k-nearest neighbor (kNN) classification algorithm based on feature similarity and symmetry is developed to detect the NLoS condition. Radio signal strength indicator (RSSI), packet reception rate (PDR) and the distance between two vehicle nodes are the features used in the proposed kNN algorithm. Moreover, due to the big data generated in VANET, secure communication between vehicle and edge node is designed using the Cuckoo filter. All obtained results are validated through well-known evaluation measures such as precision, recall, overall accuracy, and communication overhead. The results indicate that the proposed trust model has a better performance as compared to the attack-resistant trust management (ART) scheme and weighted voting (WV) approach. Additionally, the proposed trust model outperforms both ART and WV approaches under different patterns of attack such as a simple attack, opinion tampering attack, and cunning attack. Monte-Carlo simulation results also prove validity of the proposed trust model.

A Hybrid Crow Search and Gray Wolf Optimization Algorithm‐based Reliable Non‐Line‐of‐Sight Node Positioning Scheme for Vehicular Ad hoc Networks

“…The evaluation parameters considered for evaluating the proposed HCSGWOA‐NLOS‐PS are defined 27 below:…”

“…A cuckoo filter-based trust model with the merits of vehicular type, experience and plausibility is proposed for enhancing security under NLOS conditions. 27 This trust model is proposed as a reliable approach for handling network issues that emerge due to LoS and NLOS nodes that include uncertainty, incomplete and inaccurate data. It uses k-nearest neighbor classification algorithm based on the properties of symmetry and feature similarity with an objective of detecting NLOS conditions.…”

“…The evaluation phase of this data exchange approach confirms its improvement in network performance and traffic safety compared to the baseline schemes considered for investigation. A cuckoo filter‐based trust model with the merits of vehicular type, experience and plausibility is proposed for enhancing security under NLOS conditions 27 . This trust model is proposed as a reliable approach for handling network issues that emerge due to LoS and NLOS nodes that include uncertainty, incomplete and inaccurate data.…”

On the Design of Efficient Hierarchic Architecture for Software Defined Vehicular Networks

“…The proposed dynamic controller’s strategy is evaluated in a real traffic scenario and shows excellent results to reduce network changes. Soleymani et al [ 9 ] proposed a trust model for VANET based on Multi-access Edge Computing (MEC). This scheme consists of two algorithms: selecting the received information from neighboring in-vehicle messages from V2V and V2I communications.…”

Potential Using Vehicle to Vehicle Communication Based on Wireless Fidelity (Wi-Fi) for Supporting Intelligent Transportation Systems (ITS)

“…One of these is a lack of devices and equipment required for real environment. In a vehicular network, the most important challenge is the exchange of reliable and correct data among nodes [4]. In some cases, a traffic jam may be fixed in a matter of hours, while in others, it may take several days.…”