5 research outputs found
A Privacy-Preserving Fog Computing Framework for Vehicular Crowdsensing Networks
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
Recommended from our members
Computing paradigms in emerging vehicular environments: a review
Determining how to structure vehicular network environments can be done in various ways. Here, we highlight vehicle networks' evolution from vehicular ad-hoc networks (VANET) to the internet of vehicles (IoVs), listing their benefits and limitations. We also highlight the reasons in adopting wireless technologies, in particular, IEEE 802.11p and 5G vehicle-to-everything, as well as the use of paradigms able to store and analyze a vast amount of data to produce intelligence and their applications in vehicular environments. We also correlate the use of each of these paradigms with the desire to meet existing intelligent transportation systems' requirements. The presentation of each paradigm is given from a historical and logical standpoint. In particular, vehicular fog computing improves on the deficiences of vehicular cloud computing, so both are not exclusive from the application point of view. We also emphasize some security issues that are linked to the characteristics of these paradigms and vehicular networks, showing that they complement each other and share problems and limitations. As these networks still have many opportunities to grow in both concept and application, we finally discuss concepts and technologies that we believe are beneficial. Throughout this work, we emphasize the crucial role of these concepts for the well-being of humanity