University of Wollongong
Browse

A Privacy-Preserving Fog Computing Framework for Vehicular Crowdsensing Networks

Download (4.14 MB)
journal contribution
posted on 2024-11-15, 08:06 authored by Jiannan Wei, Xiaojie Wang, Nan LiNan Li, Guomin Yang, Yi Mu
Recently, the study of road surface condition monitoring has drawn great attention to improve traffic efficiency and road safety. As a matter of fact, this activity plays a critical role in the management of the transportation infrastructure. Trustworthiness and individual privacy affect the practical deployment of the vehicular crowdsensing network. Mobile sensing as well as contemporary applications is made use of problem solving. The fog computing paradigm is introduced to meet specific requirements, including mobility support, low latency, and location awareness. The fog-based vehicular crowdsensing network is an emerging transportation management infrastructure. Moreover, the fog computing is effective to reduce the latency and improve the quality of service. Most of the existing authentication protocols cannot help the drivers to judge a message when the authentication on the message is anonymous. In this paper, a fog-based privacy-preserving scheme is proposed to enhance the security of the vehicular crowdsensing network. Our scheme is secure with the security properties, including non-deniability, mutual authentication, integrity, forward privacy, and strong anonymity. We further analyze the designed scheme, which can not only guarantee the security requirements, but also achieve higher efficiency with regards to computation and communication compared with the existing schemes.

History

Citation

Wei, J., Wang, X., Li, N., Yang, G. & Mu, Y. (2018). A Privacy-Preserving Fog Computing Framework for Vehicular Crowdsensing Networks. IEEE Access, 6 43776-43784.

Journal title

IEEE Access

Volume

6

Pagination

43776-43784

Language

English

RIS ID

129528

Usage metrics

    Categories

    Keywords

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC