A Secure Trust Model Based on Fuzzy Logic in Vehicular Ad Hoc Networks With Fog Computing

In vehicular ad hoc networks (VANETs), trust establishment among vehicles is important to secure integrity and reliability of applications. In general, trust and reliability help vehicles to collect correct and credible information from surrounding vehicles. On top of that, a secure trust model can...

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Bibliographic Details
Published in:IEEE access 2017-01, Vol.5, p.15619-15629
Main Authors: Ahmad Soleymani, Seyed, Hanan Abdullah, Abdul, Zareei, Mahdi, Hossein Anisi, Mohammad, Vargas-Rosales, Cesar, Khurram Khan, Muhammad, Goudarzi, Shidrokh
Format: Article
Language:English
Subjects:
Cloud computing
Computational modeling
Data models
Edge computing
experience
fog node
Fuzzy logic
Line of sight
Mobile ad hoc networks
Nodes
plausibility
Reliability
Security
Trust
Uncertainty
VANET
Vehicles
Vehicular ad hoc networks
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Summary:In vehicular ad hoc networks (VANETs), trust establishment among vehicles is important to secure integrity and reliability of applications. In general, trust and reliability help vehicles to collect correct and credible information from surrounding vehicles. On top of that, a secure trust model can deal with uncertainties and risk taking from unreliable information in vehicular environments. However, inaccurate, incomplete, and imprecise information collected by vehicles as well as movable/immovable obstacles have interrupting effects on VANET. In this paper, a fuzzy trust model based on experience and plausibility is proposed to secure the vehicular network. The proposed trust model executes a series of security checks to ensure the correctness of the information received from authorized vehicles. Moreover, fog nodes are adopted as a facility to evaluate the level of accuracy of event's location. The analyses show that the proposed solution not only detects malicious attackers and faulty nodes, but also overcomes the uncertainty and imprecision of data in vehicular networks in both line of sight and non-line of sight environments.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2017.2733225