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A Privacy-Preserving Fog Computing Framework for Vehicular Crowdsensing Networks

Journal Article


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Abstract


  • 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.

Authors


  •   Wei, Jiannan (external author)
  •   Wang, Xiaojie (external author)
  •   Li, Nan (external author)
  •   Yang, Guomin
  •   Mu, Yi (external author)

Publication Date


  • 2018

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.

Scopus Eid


  • 2-s2.0-85050974298

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=3169&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/2163

Number Of Pages


  • 8

Start Page


  • 43776

End Page


  • 43784

Volume


  • 6

Place Of Publication


  • United States

Abstract


  • 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.

Authors


  •   Wei, Jiannan (external author)
  •   Wang, Xiaojie (external author)
  •   Li, Nan (external author)
  •   Yang, Guomin
  •   Mu, Yi (external author)

Publication Date


  • 2018

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.

Scopus Eid


  • 2-s2.0-85050974298

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=3169&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/2163

Number Of Pages


  • 8

Start Page


  • 43776

End Page


  • 43784

Volume


  • 6

Place Of Publication


  • United States