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Lee, GM and Truong, NB (2017) Trust Evaluation for Data Exchange in Vehicular Networks. In: IoTDI '17 Proceedings of the Second International Conference on Internet-of-Things Design and Implementation . pp. 325-326. (ACM/IEEE IoTDI 2017 in conjunction with CPS Week, 18 April 2017 - 21 April 2017, Pittsburgh, USA).
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Abstract
In Vehicular (Ad-hoc) Network (VANET), besides Vehicle-to-Vehicle communications (V2V), vehicles in VANET also exchange data with Road-Side-Units (RSUs) and Cellular Base Stations (Vehicle-to-Infrastructure communications (V2I)). With the introduction of Intelligent Transport Systems (ITS), VANETs possess a great potential in enabling surveillance services. �e rapid development of wireless communication technologies recently results in empowering data exchange among vehicles, RSUs and Cellular Base Stations, improving various types of applications and services such as safety driving, route planning, traffi�c alert, and context-aware infotainment. However, the bene�fits offered by VANETs and ITS cannot be fully realized unless there is a mechanism to effectively defend against fake and erroneous information exchange from malicious or dysfunctional nodes to other vehicles and RSUs for their own purposes. In this regards, trust appears as one of the solutions for VANETs to establish secure connectivity and reliable services. The conceptual idea to tackle down this challenges is that only data from trusted vehicles is taken into account. Thus, the aim is to evaluate trust of a vehicle in exchanging high quality of information. This paper presents the trust concept, key characteristics, a trust evaluation model, and a prototype for trusted data exchange activities in VANETs.
| Item Type: | Conference or Workshop Item (Paper) |
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| Uncontrolled Keywords: | Vehicular Networks; VANET; Trust; Reputation; Experience; Knowledge; Data Exchange |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Computer Science & Mathematics |
| Publisher: | ACM Digital Library |
| Date Deposited: | 09 Mar 2017 09:04 |
| Last Modified: | 13 Apr 2022 15:15 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/5833 |
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