Security, Privacy, and Fairness in Fog-Based Vehicular Crowdsensing

Abstract

Fog-based vehicular crowdsensing is an emerging paradigm where vehicles use onboard sensors to collect and share data with the aim of measuring phenomena of common interest. Unlike traditional mobile crowdsensing, fog nodes are introduced specifically to meet the requirements for location- specific applications and location-aware data management in vehicular ad hoc networks. In this article, we examine the architecture, applications, and especially security, privacy, and fairness of fog-based vehicular crowdsensing. Specifically, we first introduce the overall infrastructure and some promising applications, including parking navigation, road surface monitoring, and traffic collision reconstruction. We then study the security, privacy, and fairness requirements in fog-based vehicular crowdsensing, and describe the possible solutions to achieve security assurance, privacy preservation, and incentive fairness. By defining interesting future directions, this article is expected to draw more attention into this emerging area.