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A comparative analysis of trust models for safety applications in IoT-enabled vehicular networks

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conference contribution
posted on 2019-03-18, 13:52 authored by Farhan Ahmad, Asma AdnaneAsma Adnane, Fatih Kurugollu, Rasheed Hussain
Vehicular 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 days

Volume

11

Citation

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

© IEEE

Version

  • AM (Accepted Manuscript)

Acceptance date

2019-02-06

Publication date

2019

Notes

© 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

9781728101170

ISSN

2156-9711

eISSN

2156-972X

Language

  • en

Location

Manchester, UK