ReFIoV: a novel reputation framework for information-centric vehicular applications

Magaia, Naercio and Sheng, Zhengguo (2018) ReFIoV: a novel reputation framework for information-centric vehicular applications. IEEE Transactions on Vehicular Technology. ISSN 0018-9545

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In this article, a novel reputation framework for information-centric vehicular applications leveraging on machine learning and the artificial immune system (AIS), also known as ReFIoV, is proposed. Specifically, Bayesian learning and classification allow each node to learn as newly observed data of the behavior of other nodes become available and hence classify these nodes, meanwhile, the K-Means clustering algorithm allows to integrate recommendations from other nodes even if they behave in an unpredictable manner. AIS is used to enhance misbehavior detection. The proposed ReFIoV can be implemented in a distributed manner as each node decides with whom to interact. It provides incentives for nodes to cache and forward others’ mobile data as well as achieves robustness against false accusations and praise. The performance evaluation shows that ReFIoV outperforms state-of-the-art reputation systems for the metrics considered. That is, it presents a very low number of misbehaving nodes incorrectly classified in comparison to another reputation scheme. The proposed AIS mechanism presents a low overhead. The incorporation of recommendations enabled the framework to reduce even further detection time.

Item Type: Article
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Research Centres and Groups: Communications Research Group
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101 Telecommunication Including telegraphy, telephone, radio, radar, television
Depositing User: Zhengguo Sheng
Date Deposited: 11 Dec 2018 10:08
Last Modified: 19 Dec 2018 16:25

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