University of Sussex
Browse
RefIoV.pdf (583.8 kB)

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

Download (583.8 kB)
journal contribution
posted on 2023-06-09, 16:12 authored by Naercio Magaia, Zhengguo ShengZhengguo Sheng
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.

Funding

Doing More with Less Wiring: Mission-Critical and Intelligent Communication Protocols for Future Vehicles Using Power Lines; G2132; EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCIL; EP/P025862/1

Bionic communications and networking for connected vehicles; G2114; ROYAL SOCIETY; IE160920

History

Publication status

  • Published

File Version

  • Accepted version

Journal

IEEE Transactions on Vehicular Technology

ISSN

0018-9545

Publisher

Institute of Electrical and Electronics Engineers

Issue

2

Volume

68

Page range

1810-1823

Department affiliated with

  • Engineering and Design Publications

Research groups affiliated with

  • Communications Research Group Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2018-12-11

First Open Access (FOA) Date

2018-12-19

First Compliant Deposit (FCD) Date

2018-12-11

Usage metrics

    University of Sussex (Publications)

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC