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Open Access

Meet-Cloud for Secure and Accurate Distribution of Negative Messages in Vehicular Ad hoc Network

School of Computer and Electronic Information, Guangxi University, Nanning 530004, China.
Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23220, USA.
School of Computer Engineering and Applied Mathematics, Changsha University, Changsha 410003, China.
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Abstract

Keeping Vehicular Ad hoc Network (VANET) from attacks requires secure and efficient distribution of information about bad entities. Negative messages are pieces of information that define the negative attributes of vehicles. By formally defining the negative message, we observe that accuracy is essential for its efficient distribution. We formally define the coverage percentage and accurate coverage percentage to describe the availability and distribution efficiency of negative message. These two metrics can jointly evaluate the performance of a distribution method. To obtain both high coverage percentage and high accurate coverage percentage, we propose meet-cloud, a scheme based on meet-table and cloud computing to securely and accurately distribute negative messages in VANET. A meet-table in a Road Side Unit (RSU) records the vehicles it encounters. All meet-tables are sent to cloud service to aggregate a global meet-table. The algorithm for distributing and redistributing negative messages are designed. Security analysis shows that meet-cloud is secure against fake and holding on to negative message attacks. Simulations and analysis demonstrate that meet-cloud is secure under denial of service and fake meet-table attacks. The simulation results also justify that meet-cloud outperforms the RSU broadcast and epidemic model.

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Tsinghua Science and Technology
Pages 377-388

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Cite this article:
Huang B, Cheng X, Huang C, et al. Meet-Cloud for Secure and Accurate Distribution of Negative Messages in Vehicular Ad hoc Network. Tsinghua Science and Technology, 2018, 23(4): 377-388. https://doi.org/10.26599/TST.2018.9010016

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Received: 28 July 2017
Accepted: 14 September 2017
Published: 16 August 2018
© The authors 2018