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A comparative analysis of trust models for safety applications in IoT-enabled vehicular networks
conference contribution
posted on 2019-03-18, 13:52 authored by Farhan Ahmad, Asma AdnaneAsma Adnane, Fatih Kurugollu, Rasheed HussainVehicular Ad-hoc NETwork (VANET) is a vital transportation
technology that facilitates the vehicles to share sensitive information (such as steep-curve warnings and black ice on the road) with
each other and with the surrounding infrastructure in real-time
to avoid accidents and enable comfortable driving experience.
To achieve these goals, VANET requires a secure environment
for authentic, reliable and trusted information dissemination
among the network entities. However, VANET is prone to different attacks resulting in the dissemination of compromised/false
information among network nodes. One way to manage a secure
and trusted network is to introduce trust among the vehicular
nodes. To this end, various Trust Models (TMs) are developed
for VANET and can be broadly categorized into three classes,
Entity-oriented Trust Models (ETM), Data oriented Trust Models
(DTM) and Hybrid Trust Models (HTM). These TMs evaluate
trust based on the received information (data), the vehicle (entity)
or both through different mechanisms. In this paper, we present a
comparative study of the three TMs. Furthermore, we evaluate
these TMs against the different trust, security and quality-ofservice related benchmarks. Simulation results revealed that all
these TMs have deficiencies in terms of end-to-end delays, event
detection probabilities and false positive rates. This study can be
used as a guideline for researchers to design new efficient and
effective TMs for VANET.
History
School
- Science
Department
- Computer Science
Published in
Wireless daysVolume
11Citation
AHMAD, F. .... et al., 2019. A comparative analysis of trust models for safety applications in IoT-enabled vehicular networks. Presented at the 11th IFIP Wireless Days (WD), Manchester, UK, April 24th - 26th.Publisher
© IEEEVersion
- AM (Accepted Manuscript)
Acceptance date
2019-02-06Publication date
2019Notes
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.ISBN
9781728101170ISSN
2156-9711eISSN
2156-972XPublisher version
Language
- en